On the Boundaries of Phonology and Phonetics - Faculteit der ...

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Sponsored by
Nederlandse Vereniging voor Fonetische Wetenschappen
Center for Language and Cognition Groningen
Stichting Groninger Universiteitsfonds
Department of Linguistics, University of Groningen
1 st edition, January 2004
2 nd edition, February 2004
ISBN 90 367 1930 5

UNIVERSITY OF GRONINGEN
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Edited by
Dicky Gilbers
Maartje Schreuder
Nienke Knevel
To honour Tjeerd de Graaf

Contents
On the Boundaries of Phonology and Phonetics 7
The Editors: Dicky Gilbers, Maartje Schreuder and Nienke Knevel
Tjeerd de Graaf 15
Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
Tseard de Graaf 31
Oerset troch Jurjen van der Kooi
Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 37
Vincent J. van Heuven
The Position of Frisian in the Germanic Language Area 61
Charlotte Gooskens and Wilbert Heeringa
Learning Phonotactics with Simple Processors 89
John Nerbonne and Ivilin Stoianov
Weak Interactions 123
Tamás Bíró
Prosodic Acquisition: a Comparison of Two Theories 147
Angela Grimm
Base-Identity and the Noun-Verb Asymmetry in Nivkh 159
Hidetoshi Shiraishi
The Influence of Speech Rate on Rhythm Patterns 183
Maartje Schreuder and Dicky Gilbers
List of Addresses 203

On the Boundaries of Phonology and Phonetics
The Editors: Dicky Gilbers, Maartje Schreuder and
Nienke Knevel
In this volume a collection of papers is presented in which the boundaries
of phonology and phonetics are explored. In current phonological research,
the distinction between phonology, as the study of sound systems of
languages, and phonetics, as the study of the characteristics of human
(speech) sound making, seems to be blurred.
Consider an example of the phonological process of /l/-substitution as
exemplified in the data in Table 1.
Table 1. /l/ substitutions
Historical Dutch data:
/l/ → [w]
alt/olt oud 'old'
kalt/kolt koud 'cold'
schoo[l] schoo[w] 'school'
First Language Acquisition data (Dutch):
hallo ha[w]o 'hello'
lief [w]ief 'sweet'
blauw b[w]auw 'blue'
In phonology, the substitution segment is expected to be a minimal
deviation from the target segment. For example, boot ‘boat’ could be
realized as [pot], but not as [lot], since the target /b/ and the output [l] differ
in too many dimensions. In other words, sound substitutions should be
characterized more commonly by single feature changes than by several
feature changes. The widely attested substitution of /l/ by [w], however,
cannot be accounted for adequately as a minimal deviation from the target
based on articulatorily defined features, as shown in Figure 1.

8 The Editors: Dicky Gilbers, Maartje Schreuder and Nienke Knevel
Figure 1. /l/-substitutions
/l/ → [w]
+ son + son
+ cons - cons
+ cont + cont
+ lat - lat
- lab + lab
+ ant - ant
+ cor - cor
- high + high
- back + back
- round + round
From an acoustic point of view, liquid-glide alternations can be described
as minimal changes. The differences between the individual glides and
liquids can be related to their relative second and third formant locus
frequencies. Ainsworth and Paliwal (1984) found that in a perceptualidentification
experiment liquids such as [l] having a mid F2 locus
frequency were classified as [w] if they had a low F2 locus frequency and
as [j] if they had a high F2 locus frequency.
3160 Hz w w w l l l l j j j
↑ w w w l l l l j j j
F3 locus freq. w w w r r r l j j j
↓ w w w r r r j j j j
1540 Hz w w r r r r r j j j
760 Hz ← F2 locus freq. → 2380 Hz
Figure 2. Typical set of responses obtained from listening to glide/liquid-vowel
synthetic stimuli (after Ainsworth & Paliwal, 1984 (simplified))
Based on these acoustic characteristics, liquid-glide substitutions can be
described as a minimal change from the target, which cannot be done in the
phonological representation of these sounds. Obviously, phonology needs

On the Boundaries of Phonology and Phonetics 9
phonetic information to explain a phonological process of this kind (cf.
Gilbers, 2002).
Now consider the Dutch process of schwa insertion as exhibited in
Table 2.
Table 2. schwa insertion in Dutch
helm [��l�m] 'helmet' darm [��r�m] 'intestine'
half [��l�f] 'half' durf [��r�f] 'courage'
melk [��l�k] 'milk' hark [��r�k] 'rake'
not in: vals 'out of tune', hals 'neck', hart 'heart', start 'start'
Schwa may be inserted between a liquid /l,r/ and a non-homorganic
consonant (i.e. a consonant that differs in place of articulation with /l,r/) at
the end of a syllable. Therefore, schwa may be inserted between coronal /l/
or /r/ and non-coronal /m/, /f/, /k/, etc. Schwa is not allowed, however,
between /l/ or /r/ and a coronal obstruent /s/ or /t/. Now, Dutch has at least
two different varieties of /r/: an alveolar [r] and a uvular [�]. Since there is
no functional difference between realizations such as [���] and [���] for rat
'rat', however, there is only one phoneme /r/ in the Dutch system with its
allophones [r] and [�]. Interestingly, even Dutch speakers with a uvular [�]
do not show schwa insertion between their [�] and non-homorganic coronal
obstruent /s/ or /t/. The process of schwa insertion, apparently, takes place
before the phonetic level of actual realization of segments, i.e. on the
abstract phonological level, where /r/, /s/ and /t/ share their place feature
[coronal]. Synchronically, the process can only be described in a
phonological way, even though it may have had a phonetic - articulatory -
base originally. We assume that uvular [�] is a later variant of Dutch /r/
than coronal [r], just as the even younger, recently observed allophonic
variant [�] in Western Dutch dialects: raar 'strange' realized as [ra:�]. These
allophones date from times when the process of schwa insertion between
non-homorganic, syllable-final liquid-consonant clusters was already
'fossilized' in the Dutch system.
The above-mentioned two accounts of phonological processes indicate
the way many phonologists approach their research objects nowadays.
More and more the distinction between phonology and phonetics is
challenged in attempts to provide adequate accounts of the phonological
phenomena. In this way, the phonologists of the so-called CLCG Klankleer

10 The Editors: Dicky Gilbers, Maartje Schreuder and Nienke Knevel
group in Groningen study the phonology-phonetics interface, whereas other
members of the group cross the boundaries of phonology and phonetics by
combining the study of sound patterns with dialectology, computational
linguistics, musicology, first language acquisition or ethnolinguistics.
The Center for Language and Cognition Groningen (CLCG) is a
research institute within the Faculty of Arts of the University of Groningen.
It comprises most of the linguistic research that is being carried out within
the Faculty of Arts. One of the research groups of CLCG is this 'Klankleer'
group (Phonology and Phonetics), which focuses on the structure and
contents of the sounds of language.
This volume of papers by members of the Klankleer group is dedicated
to Tjeerd de Graaf, who was the coordinator of this group from 1999 until
2003. It does not mean that Tjeerd no longer participates in the group,
because he still supervises two PhD projects. These projects by Hidetoshi
Shiraishi and Markus Bergmann combine phonetics and phonology with
ethnolinguistics. As mentioned above, the research of most members of the
group involves combinations of different (linguistic) areas. Wilbert
Heeringa, Charlotte Gooskens and Roberto Bolognesi apply phonetics to
the study of dialectology. Nanne Streekstra is one of the first linguists in
our group who was interested in the phonology-phonetics interface. Wouter
Jansen's work is exemplary for this so-called 'laboratory phonology'. He
provides acoustic studies of voicing assimilation in obstruent clusters in
Germanic languages. Maartje Schreuder and Dicky Gilbers combine
phonetics and phonology with areas beyond linguistics, such as music
theory. Former member Klarien van der Linde and Angela Grimm study
first language acquisition, whereas Wander Lowie studies second language
acquisition. Finally, Tjeerd de Graaf started his academic life as a
researcher in theoretical physics, switched to phonetics, whereas his main
interest is now in ethnolinguistics. This homo universalis also plays piano
and oboe and speaks nine different languages. This Festschrift, however, is
dedicated to the phonetician Tjeerd de Graaf. The papers cover a wide
range of topics varying from ethnolinguistics to computational linguistics
and from first language acquisition to dialectology. The common
denominator is that all researchers work on the boundaries of phonology
and phonetics.
Vincent van Heuven, as a guest author from University of Leiden,
wonders whether certain distinctions in the speech signal are phonological
or phonetic. He investigates whether different prosodic boundary tones
form a continuum or whether they are categorical. He finds a categorical

On the Boundaries of Phonology and Phonetics 11
division between low (declarative) and non-low tones, but within the nonlow
category the cross-over from continuation to question is rather gradual.
Charlotte Gooskens and Wilbert Heeringa measured linguistic distances
between Frisian dialects and the other Germanic languages in order to get
an impression of the effect of genetic relationship and language contact on
the position of the modern Frisian language on the Germanic language
map. Wilbert is a member of the CLCG group 'Computational Linguistics'.
John Nerbonne participates as head of CLCG. His paper with Ivilin
Stoianov explores the learning of phonotactics in neural networks, in
particular the so-called Simple Recurrent Networks (SRNs). SRNs provide
a valuable means of exploring what information in the linguistic signal
could in principle be acquired by a very primitive learning mechanism.
Tamás Bíró, who is also a member of 'Computational Linguistics' and
interested in phonology, claims that the types of interactions between
languages can be extremely diverse, depending on a number of factors. The
paper analyses three case studies, namely the influence of Yiddish on
Hungarian, Modern Hebrew and Esperanto.
Angela Grimm discusses a number of empirical and theoretical
problems with respect to two models of prosodic acquisition: a template
mapping model and a prosodic hierarchy model. Both models assume that
the acquisition of word prosody is guided by universal prosodic principles.
Toshi Shiraishi discusses phonological asymmetries between nominal
and verbal stems of Nivkh, a minority language spoken on the island of
Sakhalin. These asymmetries are observed in two phonological phenomena:
consonant alternation and final fricative devoicing. Though the
asymmetries themselves look very different on the surface, Toshi's paper
makes explicit that they are subject to a common generalization, Base-
Identity.
Maartje Schreuder and Dicky Gilbers wondered whether the influence
of a higher speech rate leads to adjustment of the rhythmic pattern, as it
does in music, or just to 'phonetic compression' with preservation of the
phonological structure. An example of an item they examined is the Dutch
word perfèctioníst, which can get the rhythmic structure pèrfectioníst in fast
tempo. The results indeed showed a preference for restructured rhythms in
fast speech.
With this very diverse collection of papers, we hope to present the
phonetician Tjeerd de Graaf a representative selection of the current
activities of his CLCG-Klankleer group.

12 The Editors: Dicky Gilbers, Maartje Schreuder and Nienke Knevel
In the 1970's and 1980's Tjeerd's phonetic research stood miles away
from the feature geometries and grid representations that were customary in
phonology. He used to make sonagrams, i.e. visual displays of sound
spectrograms, of e.g. [p�], [si] and [r�]. But when the violin string of his
sonagraph broke, he wasn't able to do phonetic research anymore and that is
when ethnolinguistics stole his heart. Nowadays, it is much easier to do
phonetic analyses on the computer using programs, such as PRAAT
(Boersma and Weenink, 1992-2003). Whereas phonetics and phonology
grew apart from each other since they were installed as two distinct
disciplines of linguistics at the First International Congress of Linguists
(The Hague 1928), current laboratory phonological research may even
suggest that phonetics and phonology coincide. However, as shown in the
two examples in this introductory paper, /l/-substitution and schwainsertion,
the role of both disciplines is still distinguishable. That does not
alter the fact that co-operation between phoneticians and phonologists must
be an integral part of the study of sound patterns. Some sound phenomena,
such as ethnolinguistic and dialect differences or acquisition data, can only
be explained adequately if both phonological and phonetic characteristics
of sounds are considered.
University of Groningen, January 2004
This volume was presented to Tjeerd de Graaf on January 30, 2004 at the
workshop 'On the Boundaries of Phonology and Phonetics'. The CLCG and
the Department of Linguistics of the University of Groningen, 'de
Nederlandse Vereniging voor Fonetische Wetenschappen' and GUF
(Stichting Groninger Universiteitsfonds) sponsored this workshop. Keynote
speakers were Vincent van Heuven and Carlos Gussenhoven.
References
Ainsworth, W.A. & K.K. Paliwal (1984). Correlation between the production
and perception of the English glides /w,r,l,j/. Journal of
Phonetics, 12: 237-243.
Boersma, Paul, and David Weenink (1992-2003). PRAAT, phonetics by
computer. Available at http://www.praat.org. University of
Amsterdam.

On the Boundaries of Phonology and Phonetics 13
Gilbers, D.G. (2002). Conflicting phonologically based and phonetically based
constraints in the analysis of /l/-substitutions. In: M. Beers, P.
Jongmans & A. Wijnands (eds). Netwerk Eerste Taalverwerving,
Net-bulletin 2001. Leiden, 22-40.

Tjeerd de Graaf
Markus Bergmann, Nynke de Graaf and Hidetoshi
Shiraishi
Tjeerd de Graaf was born on January 27th 1938 in Leeuwarden, the capital
of the province Fryslân in the Netherlands. Fryslân is the largest of several
regions on the North Sea where Frisian is spoken, a West Germanic
language whose genetically closest relative is English.
Tjeerd’s parents were both Frisians, and at home they spoke exclusively
Frisian. As most other children in Fryslân at that time, Tjeerd grew up
bilingually. His first native language was Frisian, and at school he learned
Dutch, the official language of the Netherlands.
The coexistence of Frisian at home and Dutch at school was Tjeerd’s
first experience in a fascinating world of different languages. For Tjeerd,
the difference between the two languages had a very illustrative spatial
implication: when he and the other children in his neighborhood went to
school in the mornings, there was a railway crossing along the way. Once
they had crossed it they stopped speaking Frisian and switched to Dutch,
their official school language.
At the age of 18, in 1956, Tjeerd graduated from the Leeuwarden High
School and became interested in languages. His other big passion was the
science of physics and astronomy. The oldest planetarium in the world is
located in Franeker, an old academic place in Fryslân. Intrigued by the laws
governing space and time, Tjeerd studied physics at the University of
Groningen from 1956 to 1963. In 1963 he received his master’s degree in
science (Doctoraal examen) in theoretical physics, a combination of
physics, mathematics and astronomy. From 1963 until 1969 he continued
as a research associate at the Institute of Theoretical Physics at the
University of Groningen.
Tjeerd was already a “polyglot” at that time, speaking not only Frisian
and Dutch, but also German, English and French. Other languages would
follow. In the former Soviet Union the study of astronomical sciences was
enjoying an era of superiority. Tjeerd understood that learning Russian and
other East European languages would be the key to enter the field of
scientific knowledge. Along with his theoretical physics’ studies, he also

16 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
enrolled for the study of Slavic languages. The new technologies and their
application for future research fascinated him. In 1967 he received his
Master of Arts degree (Kandidaatsexamen) in Slavic languages and
computer linguistics. In the meantime, after having obtained his MS, he
continued his research in theoretical physics, combined with a study abroad
in Poland, where he lived for half a year and mastered the language.
By 1969, he finished his dissertation entitled “Aspects of Neutrino
Astrophysics”.
The cover page of Tjeerd’s dissertation in Theoretical Physics in 1969
Tjeerd’s quenchless thirst for knowledge led him to England together with
his wife Nynke and their children where they spent a year from 1970 to
1971 and where he worked as a research associate at the Institute of
Theoretical Physics at the University of Cambridge.
Upon their return to Groningen, Tjeerd became assistant professor in
physics at the Institute of Astronomy, a post he held until 1975. This was to
be a turning point in his professional career when he decided to switch to
his second passion, namely the study of languages. One of his dissertational
theses dealt with the question as to how exact a person’s identity could be
defined by his or her speech. This thesis symbolically defined one of
Tjeerd’s later linguistic interests: the aspects of spoken language, the study
of phonetics.
In 1975, Tjeerd became associate professor at the Institute of Phonetic
Sciences, Department of Linguistics, University of Groningen.
Being a native bilingual in Frisian and Dutch, Tjeerd was aware of the
numerous phonetic differences between the languages. Having studied

Tjeerd de Graaf 17
many other languages as well, Tjeerd understood how important phonetic
descriptions are not only for theoretical linguistics, but also in learning and
teaching foreign languages.
Language coexistence and language change would become another focal
point of his research. In most regions of the world, people are bilingual or
even multilingual. Language variety appears both in space and time.
Listening to radio programs or TV broadcasts dating back ten or twenty
years, reveals a distinct difference in speech as compared with today’s
custom of speaking. It is still the same language, the same place, and yet
the speech is not the same as before. Not only the lexicon of a language
changes but also the manner in which people speak, their pronunciation and
intonation. This is an extremely intriguing topic for a person interested in
languages and their varieties.
Tjeerd started to trace the oldest recordings of spoken examples of
languages. He analyzed Frisian recordings from the province of Fryslân as
well as recordings from North and East Frisian regions. Recordings of the
spoken language of former times are not only a historically important
heritage, but they also offer valuable information pertaining to language
shift processes. A practical problem with the oldest sound recordings is that
they were made on wax cylinders and their quality decreases tremendously
every time they are listened to. Tjeerd was aware of the fact that one of the
main tasks was to transfer these recordings to modern media in order to
preserve them. In the beginning of the 1990s, together with Japanese
colleagues, Tjeerd started to investigate the possibilities of preserving old
language recordings via modern audio technology. At that time, Tjeerd
acquired yet another language, namely Japanese.
Tjeerd working on wax cylinders with old recordings of Dutch

18 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
Tjeerd started to contact the most important sound archives of the world,
which are in Vienna, Berlin, and St. Petersburg. Through his collaboration
with the sound archive of the Academy of Sciences in St. Petersburg in the
1990s, he renewed his contact with Russia, which had begun with his
studies of Slavic languages in the 1960s.
After 1990, the world had experienced dramatic changes. The Iron
Curtain had disappeared and Russia had once again opened her “Window to
the West”. When Tjeerd came back to St. Petersburg in the 1990s, he was
immediately fascinated by this city he had visited for the first time some
twenty years ago when it was still known as Leningrad. As a Frisian and a
Dutchman, he felt at home there. The picturesque canals and paths along
the wide boulevards reminded him of his home region. This was no
coincidence: Czar Peter the Great, some 300 years ago, had chosen Holland
as the model for his new capital.
In the following years, Tjeerd organized joint projects with the Russian
Academy of Sciences and St. Petersburg State University to preserve and
transfer old Russian sound recordings onto modern digital audio media.
Research on a vast collection of the most various sound recordings
resulting from many linguistic field work expeditions from the end of the
XIX and XX centuries served as an incentive for several projects related to
different languages spoken in Russia.
Tjeerd started to initiate research projects on the language spoken by the
Mennonites, a group of people in Siberia, who had originally come from
regions in the Northern Netherlands and Germany and still speak the
language of their ancestors – in fact a language with great similarities to the
modern dialects spoken in North-Germany and northern parts of the
Netherlands. The Dutch press even reported about “Siberians speak
Gronings”.
Languages do not only divide people of different nations, but also build
a bridge between them. Tjeerd showed this with his research work. Even in
far-away Siberia there are people speaking almost the same language as in
Groningen. When planning his expeditions, Tjeerd was concerned with
both scientific aims and the organization of humanitarian aid from
Groningen to the Siberian villages he visited.
Language as a cultural heritage became the core of Tjeerd’s linguistic
activities. With his bilingual origin, he set the perfect example. Throughout
his life, he showed that each individual can contribute to the survival of a
language. With his Frisian wife Nynke, whom he met in his student years,
Tjeerd used to converse in Dutch. After their parents had passed away, they

Tjeerd de Graaf 19
decided to switch to Frisian. They personally experienced how a language
slowly starts to become extinct if the children do not carry on the language.
This attitude defined Tjeerd’s successive research activities in Russia.
Subsequent projects, which he coordinated now, had two goals:
documentation of endangered languages, and revitalizing and preserving
them for future generations. In the following projects, both aspects –
preservation and further development – were present. Tjeerd made several
expeditions, among others to Yakutia and the Island of Sakhalin, where he
and other linguists recorded the speech of the local indigenous peoples.
Tjeerd de Graaf with a group of speakers of indigenous languages of the Island of
Sakhalin in the Far East of Russia: Uiltas and Nivkhs, in the 1990s.
In the second half of the 1990s, Tjeerd coordinated several projects with
Institutions throughout the Russian Federation funded by the Netherlands
Organization for Scientific Research and the EU INTAS organization in
Brussels.
His main goal was to make young people aware of their unique
linguistic heritage and stimulate them in supporting minority and regional
languages. In 1998, Tjeerd was appointed Knight in the Order of the Dutch
Lion for his research and contribution in support of the preservation and
construction of databases for the minority languages in Russia. Later that
same year Tjeerd was awarded an honorary doctorate at the University of
St. Petersburg for his contribution in the joint language preservation
projects.

20 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
Tjeerd de Graaf is appointed Doctor Honoris Causa at the University of
St. Petersburg, November 1998.
Tjeerd has retired from the University of Groningen in 2003 and vacated
the chair of the coordinator of the 'Klankleer' (Phonology and Phonetics)
group of CLCG (Center of Language and Cognition Groningen). Therefore,
his colleagues compiled this Festschrift exhibiting a diversity of research
subjects on the boundaries of phonology and phonetics.
It is not a goodbye to our former coordinator. Tjeerd's passionate
engagement for languages and linguistic projects continues. Since his
retirement he became an active honoree member at the Frisian Academy in
Leeuwarden and he is still in contact with the University of St. Petersburg
for future research projects. That means more than enough commitments
for Tjeerd combined with his role as a grandfather for his five
grandchildren. Tjeerd’s enthusiasm is a stimulation for other researchers
and the young generation to continue his research.
Publications by Tjeerd de Graaf
1966
The Annihilation of a Neutrino-antineutrino Pair into Photons and the
Neutrino Density in the Universe. (With H.A.Tolhoek). Nuclear physics,
81: 596 and 99: 695.
Neutrinoprocessen en Neutrino-astronomie [Neutrino Processes and
Neutrino Astronomy]. Internal Report IR 68, Natuurkundig Laboratorium
Groningen. 58 pp.

Tjeerd de Graaf 21
1968
De Rol van het Neutrino in de Astrofysica [The Role of the Neutrino in
Astrophysics]. Nederlands tijdschrift voor natuurkunde, 34: 329.
Phase Factors in Discrete Symmetry Operations. (With H.A.Tolhoek).
Intern Rapport IR 85, Natuurkundig Laboratorium Groningen, 96 pp.
Detectie van Neutrino's uit de Zon [Detection of Solar Neutrinos].
Nederlands tijdschrift voor natuurkunde, 34: 357.
1969
Phase Factors in Quantum Field Theory. Physica, 43: 142.
Muonen uit Kosmische Straling: het Utah Experiment [Muons from
Cosmic Radiation: the Utah Experiment]. (With J. van Klinken).
Nederlands tijdschrift voor natuurkunde, 36: 301.
Aspects of Neutrino Astrophysics. Dissertation University of Groningen.
Groningen. 119 pp.
Syllabus Beknopte Theoretische Natuurkunde [Syllabus Summary of
Theoretical Physics]. Natuurkundig Laboratorium Groningen, 190 pp.
1970
On a Cosmic Background of Low-energy Neutrinos. Astronomy and
Astrophysics, 5: 335.
Neutrino Processes in the Lepton Era of the Universe. Lettere al Nuovo
Cimento, 4: 638.
Cosmological Neutrinos. Proceedings of the Cortona Meeting on
Astrophysical Aspects of the Weak Interactions, 81.
1971
Nucleaire Astrofysica in het Laboratorium [Laboratory Nuclear
Astrophysics]. Nederlands tijdschrift voor natuurkunde, 38: 107.
The Astrophysical Importance of Heavy Leptons. Lettere al Nuovo
Cimento, 2: 979.
1972
Lecture Notes on Nuclear Astrophysics. Scuola Normale Superiore, Pisa,
45 pp.
The Lepton Era of the Big Bang. Proceedings of the Europhysics
Conference Neutrino'72. Budapest, 167.

22 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
1973
Neutrinos in the Universe. Vistas in Astronomy, 15: 161.
1974
Nuclear Processes in the Early Universe. VI th International Seminar on
Nuclear Reactions in the Cosmos. Leningrad, 329.
Kernenergie in de Kosmos [Nuclear Energy in the Cosmos]. Atoomenergie
en haar Toepassingen, 81.
De Heliumabundantie in het Heelal [The Helium Abundance in the
Universe]. (With W.J. Weeber). Nederlands tijdschrift voor natuurkunde,
40: 183.
1977
De Computer en de Faculteit der Letteren [The Computer and the Faculty
of Arts]. Informatiebulletin Computercommissie FdL. Groningen, 38 pp.
1978
Vowel Analysis with the Fast Fourier Transform. Acustica, 41: 41
Ienlûd, twa lûden, twalûden [Monophthongs, Two Sounds, Diphthongs].
(with G.L. Meinsma). Us Wurk, 27: 81.
Analyse de voyelles avec des méthodes digitales [Vowel Analysis with
Digital Methods]. Actes des 9èmes Journées d'Etude sur la Parole.
Lannion, 233.
Linear Prediction in Speech Research. Prace XXV Seminarium Otwartego z
Akustyki. Poznań, 19.
1979
Het kenmerk bij hoge gespannen vokalen [The Feature
in High Tense Vowels]. (With N.Streekstra). TABU, 8: 40.
De Computer en Fonetisch Onderzoek [The Computer and Phonetic
Research]. Informatiebulletin Computercommissie FdL. Groningen, 5 pp.
Vowel Analysis with Linear Prediction. Proceedings of the 9th
International Congress of Phonetic Sciences. Copenhagen, 265.
Digital Methods for the Analysis of Speech. Proceedings of the 7th
Colloquium on Acoustics. Budapest, 289.
1980
Phonetic Aspects of Breaking in West Frisian. (With P.Tiersma).
Phonetica, 37: 109.

Tjeerd de Graaf 23
De brekking fan sintralisearjende twalûden yn it Frysk [Breaking of
Centralizing Diphthongs in Frisian]. (With G.L. Meinsma). Us Wurk, 29:
131.
Vannak-e Diftongusok a Magyar Köznyelvben? [Are there Diphthongs in
Standard Hungarian?]. (With A.D. Kylstra). Nyelvtudományi Közlemények,
82: 313.
Applications of Linear Predictive Coding in Speech Analysis. Proceedings
of the Symposium on Speech Acoustics, 57.
1981
Wiskundige Modellen in het Spraakonderzoek [Mathematical Models in
Speech Research]. Wiskundige Modellen: Cursusboek Stichting TELEAC,
165.
Syllabegrenzen en Fonetische Experimentatie [Syllable Boundaries and
Phonetic Experiments]. GLOT, Tijdschrift voor Taalwetenschap, 4: 229.
Book Review of: Metrical Myths – An Experimental-Phonetic
Investigation into the Production and Perception of Metrical Speech.
Spectator, 10: 385.
1982
Vowel Contrast Reduction in Japanese Compared to Dutch. (With F.J.
Koopmans-van Beinum). Proceedings of the Institute of Phonetic Sciences .
Amsterdam, 7: 27.
A Sociophonetic Study of Language Change. Proceedings of the 13 th
International Conference of Linguistics. Tokyo, 602.
1983
Phonetic Sciences in the Netherlands, Past and Present. (With other
authors). Publication of the Netherlands Association for Phonetic Sciences.
Dordrecht, 32 pp.
On the Reliability of the Intraoral Measuring of Subglottal Pressure. (With
G.L.J. Nieboer and H.K. Schutte). Proceedings of the 10 th International
Congress of Phonetic Sciences. Utrecht, 367.
Phonetic Aspects of Vowels and Breaking of Diphthongs. Fifth
International Phonology Meeting. Eisenstadt, 98.
Vowel Contrast Reduction in Finnish, Hungarian and Other Languages.
Dritte Tagung für Uralische Phonologie. Eisenstadt, 11.

24 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
1984
Vowel Contrast Reduction in Terms of Acoustic System Contrast. (With
F.J. Koopmans-van Beinum). Proceedings of the Institute of Phonetic
Sciences. Amsterdam, 8: 41.
Vokaalduur en Breking van Diftongen in het Fries [Vowel Duration and
Breaking of Diphthongs in Frisian]. Verslagen van de Nederlandse
Vereniging voor Fonetische Wetenschappen, 54.
The Acoustic System Contrast and Vowel Contrast Reduction in Various
Languages. Proceedings of the 23 rd Acoustic Conference on Physiological
and Psychological Acoustics. Madrid, 76.
Vowel Data Bases. (With A. Bladon en M. O'Kane). Speech
Communication, 3: 169.
Nederlandse Leerboeken voor de Fonetiek van het Engels [Dutch Teaching
Methods on the Phonetics of English]. (With A. van Essen en J.
Posthumus). Toegepaste Taalwetenschap in Artikelen, 20: 123-154.
1985
Phonetic Aspects of the Frisian Vowel System. NOVELE, 5: 23-42.
Review of: Spreken en Verstaan, een nieuwe Inleiding tot de Experimentele
Fonetiek [Speaking and Understanding, A New Introduction to
Experimental Phonetics]. (By S. Nooteboom en A. Cohen). Logopedie en
Foniatrie, 57: 106.
De Groninger Button [The Groningen Button]. (With G.L.J. Nieboer and
H.K. Schutte). Verslagen van de Nederlandse Vereniging voor Fonetische
Wetenschappen, 57-62.
1986
Sandhi Phenomena in West Frisian. (With G. van der Meer). Sandhi
Phenomena in the Languages of Europe. Berlin, 301-328.
Review of: The Production of Speech. (By P.F. MacNeilage). Studies in
Language, 10: 273-277.
Production of Different Types of Esophageal Voice Related to the Quality
and the Intensity of the Sound Produced. Folia Phoniatrica, 38: 292.
De Uitspraak van het Nederlands door Buitenlanders [The Pronunciation of
Dutch by Foreigners]. Logopedie en Foniatrie, 58: 343-349.
Sociophonetic Aspects of Frisian. Friser Studier IV/V. Odense, 3-21.
Een contrastief fonetisch onderzoek Japans-Nederlands [A Contrastive
Phonetic Research Japanese-Dutch]. Verslagen van de Nederlandse
Vereniging voor Fonetische Wetenschappen, 15-24.

Tjeerd de Graaf 25
1987
The Retrieval of Dialect Material from Old Phonographic Wax Cylinders.
Proceedings of the Workshop on “New Methods in Dialectology”.
Amsterdam, 117-125.
Acoustic and Physiological Properties of the Laryngeal and Alaryngeal
(Esophageal) Voice. Proceedings of the XXXIV th Open Seminar on
Acoustics. Wrocław, 10-16.
A Contrastive Study of Japanese and Dutch. Proceedings of the XI th
International Congress of Phonetic Sciences. Tallinn, 124-128.
1988
His Master's Voice: Herkenning van de Spraakmaker [His Master’s Voice:
Recognition of the Speech Producer]. TER SPRAKE: SPRAAK als
betekenisvol geluid in 36 thematische hoofdstukken. Dordrecht, 200-208.
Book Review: Fonetiek en Fonologie [Phonetics and Phonology]. (By R.
Collier en F.G. Droste). Logopedie en Foniatrie, 60: 195.
The Frisian Language in America. (With T. Anema and H. Schatz).
NOWELE, 6: 91-108.
Esophageal Voice Quality Judgements by Means of the Semantic
Differential. (With G.L.J. Nieboer and H.K. Schutte). Journal of Phonetics,
16: 417-436.
Book Review: Sprechererkennung [Speaker Recognition]. (By Hermann J.
Künzel). Journal of Phonetics, 16: 459-463.
1989
Reconstruction, Signal Enhancement and Storage of Sound Material in
Japan. Proceedings of the 2 nd International Conference on Japanese
Information in Science, Technology and Commerce. Berlin, 367-374.
Aerodynamic and Psycho-acoustic Properties of Esophageal Voice
Production. (With G.L.J. Nieboer and H.K. Schutte). Proceedings of the
Conference on Speech Research '89. Budapest, 53-58.
A Data Base of Old Sound Material. Proceedings of the ESCA Workshop
on Speech Input/Output Assessment and Speech Data Bases. Noordwijk,
2.14.1-5.
1990
Een contrastief fonetisch onderzoek, in het bijzonder Japans-Nederlands
[Contrastive Phonetic Research, in Particular Japanese-Dutch]. Neerlandica
Wratislaviensia IV. Wrocław, 140-148.

26 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
Book Review: To Siberia and Russian America, Three Centuries of Russian
Eastward Expansion. Circumpolar Journal, 7: 41-46.
New Technologies in Sound Reconstruction and their Applications to the
Study of the Smaller Languages of Asia. Proceedings of the IV th
International Symposium “Uralische Phonologie”. Hamburg, 15-19.
GARASU-GLAS: Fonetische contrasten Japans-Nederlands [GARASU-
GLAS: Phonetic Contrasts Japanese-Dutch]. TABU. Bulletin voor
Taalwetenschap, 20: 49-57.
1991
Aerodynamic and Phonetic Properties of Voice Production with the
Groningen Button. TENK jaarboek, 91-97.
Laser-beam Technology in Diachronic Phonetic Research and
Ethnolinguistic Field Work. Proceedings of the XII th International
Congress of Phonetic Sciences. Amsterdam, 114-118.
Laut aus Wachs: Der Übergang von stoffgebundenen zum elektronischen
und optischen Informationstransport [Sound from Wax: The Transition
from Material-Bound to Electronic and Optic Information Transport]. TU
International. Berlin, 14/15: 63-66.
1992
The Languages of Sakhalin. Small Languages and Small Language
Communities: News, Notes, and Comments. International Journal of the
Sociology of Languages, 94: 185-200.
Dutch Encounters with Sakhalin and with the Ainu People. Proceedings of
the International Conference 125 th anniversary of the birth of Bronis�aw
Pi�sudski. Sapporo, 108-137.
The Ethnolinguistic Situation on the Island of Sakhalin. Circumpolar
Journal, 6: 32-58.
Aerodynamische en fonetische eigenschappen van verschillende soorten
slokdarmstem [Aerodynamic and Phonetic Features of Different Kinds of
Esophageal Voice]. (With G.L.J. Nieboer and H.K. Schutte). Klinische
Fysica, 8: 64-66.
The Dutch Role in the Border Area between Japan and Russian. Round Table
Conference “The Territorial Problem in Russo-Japanese Relations”.
Moscow, 20-26.
De Taal der Mennonieten [The Language of the Mennonites]. Syllabus
NOMES Symposium Groningen, 42 pp.

Tjeerd de Graaf 27
1993
Saharin ni okeru shoosuu minzoku no gengo jookyoo [The Status of
Minority Languages on Sakhalin]. (With K. Murasaki). Japanese Scientific
Monthly, 46: 18-24.
The Ethnolinguistic Situation on the Island of Sakhalin. Ethnic minorities
on Sakhalin. Yokohama, 13-32.
Vstrechi gollandtsev c Sakhalinom i Ainami [Meetings of the Dutch with
Sakhalin and the Ainu Population]. Proceedings of the International
Conference “B.O. Pilsudski - issledovatel' narodov Sakhalina”. Yuzhno-
Sakhalinsk, 92-99.
De taal der Mennonieten in Siberië en hun relatie met Nederland [The
Language of the Siberian Mennonites and their Relation with the
Netherlands]. (With R. Nieuweboer). Doopsgezinde Bijdragen, 19: 175-
189.
Languages and Cultures of the Arctic Region in the Former Soviet Union.
(With R. Nieuweboer). Circumpolar Journal, 1-2: 29-42.
1994
The Dutch Role in the Border Area between Japan and Russia.
Circumpolar Journal, 3-4: 1-12.
Nederlands in Siberië [Dutch in Siberia]. (With R. Nieuweboer). TABU
Taalkundig Bulletin, 24: 65-75.
The Language of the West Siberian Mennonites. (With R. Nieuweboer).
RASK, Internationalt tidsskrift for sprog og kommunikation, 1: 47-63.
1995
Het territoriale geschil tussen Japan en Rusland over de Koerilen [The
Territorial Dispute between Japan and Russia about the Kuril Islands].
(With I. van Oosteroom). Internationale Spectator, 49: 41-46.
Dutch Encounters with Sakhalin and with the Ainu People. Linguistic and
�������� ������� ���� ������, 35-61.
The Language of the West Siberian Mennonites. (with R. Nieuweboer).
Proceedings of the XIII th Congress of Phonetics Sciences. Stockholm, 4:
180-184.
Pitch Stereotypes in the Netherlands and Japan. (With R. van Bezooijen
and T. Otake). Proceedings of the XIII th Congress of Phonetic Sciences.
Stockholm, 680-684.
The Reconstruction of Acoustic Data on the Ethnic Minorities of Siberia.
Proceedings of the International Conference on “The Indigenous Peoples

28 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
of Siberia: Studies of Endangered Languages and Cultures”. Novosibirsk,
1: 381-383.
1996
Book Review of: Joshua A. Fishman, Yiddish: Turning to Life. Studies in
Language, 20,1: 191-196.
Language Minorities in the Sakha Republic (Yakutia). Report Nagoya City
University. Nagoya, 165-179.
Dutch Encounters with the Peoples of Eastern Asia. A Frisian and Germanic
Miscellany, published in Honour of Nils Århammar on his Sixty-Fifth
Birthday. Odense, 377-386.
Dutch Immigrants in Siberia? The Language of the Mennonites. Charisteria
doctissimo Přemysl Janota oblata, Acta Universitatis Carolinae
viro
Philologica. Prague, 75-86
Archives of the Languages of Russia. (With L.V. Bondarko). Reports on the
INTAS Project No. 94-4758. St.-Petersburg, 120 pp.
1997
The Reconstruction of Acoustic Data and Minority Languages in Russia.
Proceedings of the 2 nd International Congress of Dialectologists and
Geolinguists. Amsterdam.,44-54.
Language and Culture of the Russian Mennonites. Around Peter the Great.
Three Centuries of Russian-Dutch Relations. Groningen, 132-142.
Resten van het Jiddisch in Groningen en Sint-Petersburg [Remnants of the
Yiddish Language in Groningen and Saint-Petersburg]. VDW-berichten,
Vereniging voor Dialectwetenschap, 1: 6-7.
The Reconstruction of Acoustic Data and the Study of Language Minorities
in Russia. Language Minorities and Minority Language. ������� ��������
1998
Linguistic Databases and Language Minorities around the North Pacific
Rim. Lecture on the Occasion of the Doctorate Honoris Causa, St.-
Petersburg, 14 pp.
Linguistic Databases: A Link between Archives and Users. Journal of the
International Association of Sound Archives, 27-34.

Tjeerd de Graaf 29
1999
Russian-Yiddish: Phonetic Aspects of Language Interference. (With N.
Svetozarova, Yu. Kleiner and R. Nieuweboer). Proceedings of the 14 th
International Congress of Phonetic Sciences. San Francisco., 1397-1401.
Language Contact and Sound Archives in Russia. (With L. Bondarko).
Proceedings of the 14 th International Congress of Phonetic Sciences. San
Francisco, 1401-1404.
Lingvisticheskie bazy dannykh i yazykovye men’shinstva po obeim storonam
severnogo tikho-okeanskogo poyasa [Linguistic databases and language
minorities at both sides of the North-Pacific Rim]. Yazyk i rechevaya
deyatel’nost’, 2: 8-18.
2000
Scientific Links between Russia and The Netherlands: A Study of the
Languages and Cultures in the Russian Federation. Proceedings of the
Conference on the Netherlands and the Russian North. Arkhangelsk. To be
published.
The Language of the Siberian Mennonites. (With R. Nieuweboer). New
Insights in Germanic Linguistics II. Frankfurt am Main, 21-34.
2001
Nivkh and Kashaya: Two endangered Languages in Contact with Russian and
English. Materialy mezhdunarodnoy konferentsii “100 let eksperimental’noy
fonetike v Rossii”. St.-Petersburg, 78-83.
Data on the Languages of Russia from Historical Documents, Sound Archives
and Fieldwork Expeditions. Recording and Restoration of Minority
Languages, Sakhalin Ainu and Nivkh, ELPR Report A2-009. Kyoto, 13-37.
Kashaya Pomo and the Russian Influence around the North Pacific. Materials
��� ����� ������������� ���������� �� ��������� ��������� ��� ���
��
Scholarly Heritage. Kraków, 385-395.
2002
Yazyk i etnos [Language and Ethnos]. (With A.S. Gerd and M. Savijärvi).
Texts and Comments on Balto-Finnic and Northwestern Archaic Russian
Dialects. St.-Petersburg, 206 pp.
Voices from Tundra and Taiga: Endangered Languages in Russia on the
Internet. Conference Handbook on Endangered Languages. Kyoto, 57-79.

30 Markus Bergmann, Nynke de Graaf and Hidetoshi Shiraishi
Phonetic Aspects of the Frisian Language and the Use of Sound Archives.
Problemy i metody eksperimental’no-foneticheskikh issledovaniy. St.-
Peterburg, 52-57.
Voices from the Shtetl: The Past and Present of the Yiddish Language in
Russia. Final Report NWO Russian-Dutch Research Cooperation.
Groningen, 143 pp.
The Use of Sound Archives in the Study of Endangered Languages. Music
Archiving in the World, Papers Presented at the Conference on the
Occasion of the 100 th Anniversary of the Berlin Phonogramm-Archiv.
Berlin, 101-107.
The Use of Acoustic Databases and Fieldwork for the Study of the
Endangered Languages of Russia. Proceedings of the International LREC
Workshop on Resources and Tools in Field Linguistics. Las Palmas, 29.1-4
(CD-ROM).
Yiddish in St.-Petersburg: The Last Sounds of a Language. Proceedings of
the Conference “Klezmer, Klassik, jiddisches Lied. Jüdische Musik-Kultur
in Osteuropa.”. Potsdam. To be published.
2003
Yazyki severnoy i vostochnoy Tartarii – o yazykovykh svedeniyakh v
knige N. Vitsena [The Languages of North and East Tartary – About the
Linguistic Data in the Book of N. Witsen]. (With M. Bergmann).
Proceedings of the Conference on General Linguistics. St.-Petersburg. To
be published.
Description of Minority Languages in Russia on the Basis of Historical
Data and Fieldwork. Proceedings of the XVI th International Congress of
Linguists. Prague. To be published.
Voices of Tundra and Taiga: Data on Minority Languages in Russia from
Historical Data and Fieldwork. Proceedings of the Conference “Formation
of Educational Programs Aimed at a New Type of Humanitarian Education
in Siberian Polyethnic Society, Novosibirsk. To be published.
Endangered Languages in Europe and Siberia: State of the Art, Needs and
Solutions. International Expert Meeting on UNESCO Programme
“Safeguarding of Endangered Languages”. Paris. To be published
Presentation of the UNESCO Document “Language Vitality and
Endangerement”. Focus on Linguistic Diversity in the New Europe.
European Bureau for Lesser Used Languages, Brussels. To be published.

Tseard de Graaf
Oerset troch Jurjen van der Kooi
Tseard de Graaf is berne op 27 jannewaris 1938 yn Ljouwert, de haadstêd
fan de Nederlânske provinsje Fryslân, de grutste regio oan de kusten fan de
Noardsee dêr’t it Frysk, in Westgermaanske taal mei as neiste sibbe it
Ingelsk, sprutsen wurdt.
Tseard syn âlden wiene beide Fries en thús waard allinne Frysk praat.
Syn earste taal wie dan ek it Frysk; op skoalle learde er it Nederlânsk, de
offisjele taal fan Nederlân.
It Frysk waard doe noch net op skoalle jûn en de bern moasten dêr
Nederlânsk leare. Troch it neistinoar fan it Frysk thús en it Nederlânsk op
skoalle kaam Tseard foar it earst yn ’e kunde mei de fassinearjende wrâld
fan ûnderskate talen. Foar Tseard hie it ûnderskie tusken dy twa talen ek in
besûnder romtlik aspekt. As hy en de oare bern út syn buert de moarns nei
skoalle ta gyngen moasten se oer it spoar. Wiene se dêr oer, dan giene se
fan it Frysk oer op it Nederlânsk, de taal fan de skoalle.
18 Jier âld wie er, doe’t er yn 1956 eineksamen middelbere skoalle die
en hy krige niget oan talen. Syn oare grutte leafdes wiene natuer- en
stjerrekunde. It âldste planetarium fan de wrâld is yn Frjentsjer, de âlde
akademystêd fan Fryslân. Yn ’e besnijing fan de wetten dy’t tiid en romte
regeare, studearre Tseard fan 1956 oant 1963 natuerkunde oan de
Universiteit fan Grins. Yn dat lêste jier die er doktoraal eksamen teoretyske
natuerkunde, in kombinaasje fan natuerkunde, wiskunde en stjerrekunde en
dêrnei wie er oant 1969 ûndersykmeiwurker oan it Ynstitút foar Teoretyske
Natuerkunde fan de Grinzer universiteit.
Tseard wie doe al in ‘polyglot’. Hy spruts net allinne Frysk en
Nederlânsk, mar ek Dútsk, Ingelsk en Frânsk. Oare talen soene folgje. Yn
de Sowjet Uny stie de stúdzje fan de astronomy doe op in superieur nivo.
Tseard seach dat en learde it Russysk en oare Eastjeropeeske talen om’t dy
it paad nei nij ynsjoch yn dy fjilden fan wittenskip iepenleinen. Neist syn
stúdzje fan de teoretyske natuerkunde folge er kolleezjes yn de Slavyske
talen. Hy wie fassinearre troch de nije technologyen en har
tapassingsmooglikheden foar takomstich ûndersyk en yn 1967 die er
kandidaatseksamen Slavyske talen en kompjutertaalkunde. Yntysken wie er

32 Oerset troch Jurjen van der Kooi
nei syn doktoraal natuerkunde trochgien mei syn ûndersyk yn de teoretyske
natuerkunde, dat er kombinearre mei in heal jier stúdzje yn Poalen, dêr’t er
ek it Poalsk by learde.
Yn 1969 wie er klear mei syn dissertaasje, titele: “Aspects of Neutrino
Astrophysics”.
It titelblêd fan Tseard syn dissertaasje teoretyske natuerkunde út 1969
Syn ûndwêstbere toarst nei witten brocht Tseard nei Ingelân, dêr’t er mei
frou en bern fan 1970 oant 1971 in jier tabrocht en dêr’t er wurke as
ûndersiker oan it Ynstitút foar Teoretyske Natuerkunde fan de Universiteit
fan Cambridge.
Werom yn Grins waard Tseard universitêr meiwurker natuerkunde oan
it Ynstitút foar Astronomy. Oant 1975. Dat jier waard in kearpunt yn syn
wittenskiplike karriêre om’t er besleat de wei fan syn twadde grutte leafde
te gean, dy fan de bestudearring fan talen. Ien fan de stellingen by syn
dissertaasje gie oer de fraach, hoe krekt of immens identiteit definiearre
wurde kin troch syn of har taal. Dy stelling kin sjoen wurde as in
symboalyske paadwizer nei syn lettere wei yn de taalkunde, dy’t him liede
soe nei de stúdzje fan aspekten fan de sprutsen taal, nei de fonetyk.
Yn 1975 waard Tseard meiwurker oan it Ynstitút foar Fonetyk fan de
Literêre Fakulteit fan de Grinzer universiteit.
Om’t er fan jongs ôf oan twatalich wie (Frysk-Nederlânsk) hie Tseard in
skerp each foar de ûntelbere fonetyske ferskillen tusken dy talen. En om’t
er oare talen bestudearre hie, wist er hoe wichtich fonetyske beskriuwingen
binne, net allinne foar de teoretyske taalkunde, mar likegoed ek foar it
learen fan en it lesjaan yn frjemde talen.

Tseard de Graaf 33
It neistinoar fan talen en taalferoaring soene oare swiertepunten fan syn
ûndersyk wurde. Rûnom yn ’e wrâld binne minsken twa- of sels meartalich.
Der is taalfariaasje yn romte èn yn tiid. Harket men nei radioprogramma’s
of tillevyzje-útstjoerings fan tsien of twintich jier lyn, dan heart men in oare
sprektaal as at no gongber is. It is noch altiten deselde taal en itselde plak,
en dochs is de taal net mear gelyk. Net allinne it leksikon fan in taal
feroaret, mar ek minskene wize fan sprekken, de útspraak en de yntonaasje,
in útsûnderlik nijsgjirrich ûnderwerp foar immen dy’t niget hat oan talen en
har fariabiliteit.
Tseard begûn mei in syktocht nei de âldste registraasjes op lûddragers
fan sprutsen taal. Hy analysearre materiaal net allinne út Westerlauwersk
mar ek út Noard- en Eastfryslân. Sokke registraasjes fan eardere sprutsen
taal binne net allinne wichtich histoarysk erfguod, mar se jouwe ek
weardefolle ynformaasje oangeande taalferoaringsprosessen. In praktysk
probleem by dy âldste lûdregistraasjes is dat se makke binne op
waakssilinders en dat de kwaliteit hurd ôfnimt elke kear as se beharke
wurde. Tseard seach yn dat it fan it grutste belang is en bring dizze
registraasjes oer op moderne lûddragers, sadat se bewarre bliuwe. Yn it
begjin fan de jierren 90 begûn Tseard mei kollega’s út Japan in ûndersyk
nei de mooglikheden dêrta. Yn dy tiid makke er him noch in taal eigen, it
Japansk, dat er floeiend sprekken learde.
Tseard oan ’e skrep mei waakssilinders mei âlder Nederlânsk
Tseard socht kontakt mei de wichtichste lûdargiven yn de wrâld, dy yn
Wenen, Berlyn en Sint Petersboarch. Troch syn oparbeidzjen yn de 90er
jierren mei it lûdargyf fan de Akademy fan Wittenskippen yn dy lêste stêd

34 Oerset troch Jurjen van der Kooi
luts er op ’en nij de relaasjes oan mei Ruslân, dy’t yn de 60er jierren begûn
wiene mei syn bestudearring fan de Slavyske talen.
Sûnt 1990 is wrâld dramatysk feroare. It Izeren Gerdyn is der net mear
en Ruslân hat opnij syn ‘Finster op it Westen’ iepenset. Doe’t Tseard nei
1990 weromkaam yn Sint Petersboarch rekke er daliken fassinearre troch
dizze stêd dy’t er foar it earst likernôch 20 jier lyn, doe’t er noch Leningrad
hiet, sjoen hie. As Fries en Nederlanner fielde er him der thús. De
skildereftige kanalen en paden lâns de wide bûlevaren diene him tinke oan
thús. Dat wie gjin tafal: tsaar Peter de Grutte hie sa’n 300 jier earder Hollân
keazen as model foar syn nije haadstêd.
Tseard organisearre no mienskiplike projekten mei de Russyske
Akademy fan Wittenskippen en de Steatsuniversiteit fan Sint Petersboarch.
It doel wie âlde Russyske lûdregistraasjes te bewarjen en oer te setten op
moderne digitale audio media. Undersyk nei in grutte samling fan
alderhande lûdregistraasjes, resultaat fan withoe folle linguïstyske
ekspedysjes fan ein 19de en út de 20ste ieu, brocht nije projekten
ûnderskate yn Ruslân spruten talen oanbelangjende op ’e gleed.
Sels sette er útein mei ûndersyk nei de taal fan de Sibearyske
Mennoniten, dêr’t it komôf fan socht wurde moat yn noardlik Nederlân en
Dútslân en dy’t noch altiten de taal fan de foarâlden sprekke – feitliken in
taal dy’t gâns hat fan de dialekten fan it hjoeddeiske Noard-Dútslân en de
noardlike parten fan Nederlân. De Nederlânske parse kaam sels mei de kop
“Sibeariërs sprekke Grinzers”.
Talen skiede net allinne folken en naasjes, se bouwe der ek brêgen
tusken. Tseard liet ek dat mei syn ûndersyk sjen. Sels yn it fiere Sibearje
wenje minsken dy’t likernôch deselde taal hawwe as de minsken yn
Grinslân. By it plannen fan syn ekspedysjes tocht Tseard net allinne oan de
wittenskip, mar ek om de minsken: hy organisearre ek humanitêre help út
Grins wei foar de doarpen dy’t er yn Sibearje oandie.
Taal as kultureel erfskip waard de kearn fan syn linguïstyske
aktiviteiten. Troch syn twatalich komôf koe er in treflik foarbyld jaan. Syn
hiele libben hat er sjen litten dat elk yndividu bydrage kin oan it oerlibjen
fan in taal. Mei syn frou Nynke, dy’t er met hie yn syn studintetiid en dêr’t
er lang allinne Nederlânsk mei praat hie, praatte er no ôf om oer te stappen
op it Frysk. Nynke is sels in Friesinne en nei de dea fan har âlden murken
hja sels hoe't in taal stadichoan út begjint te stjerren as de bern him net
fierder trochjouwe.
Dy taalhâlding waard de rjochtline foar Tseard syn opienfolgjende
ûndersykaktiviteiten yn Ruslân. De projekten dy’t er fan dat stuit ôf oan

Tseard de Graaf 35
koördinearre krigen twa doelstellings: bedrige talen net allinne
dokumintearje, mar ek revitalisearje en yn stân hâlde foar kommende
generaasjes. Tseard die mei oan ferskate ekspedysjes, ûnder oaren nei
Yakutia en it eilân Sakhalin, wêr’t er mei oare linguïsten de talen fan de
lokale folken fêstlei.
Tseard de Graaf mei sprekkers fan talen fan it eilân Sakhalin yn it fiere easten fan
Ruslân: Uiltas en Nivkhs (jierren 90)
Yn de twadde helte fan de 90er jierren koördinearre Tseard ûnderskate
projekten mei ynstituten rûnom yn de Russyske Federaasje, foar de
finansearring soargen Nederlânske wittenskiplike organisaasjes en de
INTAS fan de EU.
Alderearsten woe er jonge minsken bybringe dat harren taal in unyk
erfskip is en dat hja minderheids- en regionale talen stypje moatte. Yn 1998
waard Tseard beneamd ta ridder yn de oarder fan de Nederlânske liuw
fanwegens syn ûndersyk nei en krewearjen foar it behâld fan en it opsetten
fan databanken foar de minderheidstalen yn Ruslân. Letter datselde jiers
krige er in earedoktoraat fan de Universiteit fan Sint Petersboarch foar syn
bydragen oan de mienskiplike taalbehâldprojekten.

36 Oerset troch Jurjen van der Kooi
Tseard de Graaf earedoktor oan de Universiteit fan Sint Petersboarch, novimber
1998.
Tseard moast yn 2003 mei pinsjoen en syn plak as koördinator fan de
ôfdieling 'Klanklear' (Fonology en Fonetyk) fan it CLGC (Center of
Language and Cognition Groningen) fan de Grinzer Universiteit opjaan. Ta
dy gelegenheid ha syn kollega's dizze earebondel mei in ferskaat oan
bydragen oer ûndersyk yn de grinsgebieten fan fonology en fonetyk
gearstald.
Lykwols, it is gjin ôfskie fan ús eardere koördinator. Tseard syn
pasjonearre belutsenens by talen en linguïstyske projekten is bleaun. Sûnt
syn pinsjoen is er aktyf as honorêr meiwurker fan de Fryske Akademy yn
Ljouwert en ek it kontakt mei de Universiteit fan Sint Petersboarch oer
ûndersyksprojekten dy't op kommende wei binne is bleaun. Dat betsjut dat
Tseard neist syn rol as pake foar syn fiif pakesizzers noch genôch te dwaan
hat. Syn entûsjasme is in oantrún foar (kommende) ûndersikers om fierder
te gean mei it ûndersyk dat hy op priemmen set hat.

Boundary Tones in Dutch: Phonetic or Phonological
Contrasts?
Vincent J. van Heuven
1. Introduction 1
1.1. Linguistic categorization of sound
A basic problem of linguistic phonetics is to explain how the infinite
variety of speech sounds in actual utterances can be described with finite
means, such that they can be dealt with in the grammar, i.e. phonology, of a
language. The crucial concept that was developed to cope with this
reduction problem is the sound category, or – when applied to the
description of segmental phenomena – the phoneme. This is best conceived
of as an abstract category that contains all possible sounds that are mutually
interchangeable in the context of a minimal word pair. That is, substitution
of one token (allophone) of a phoneme for an other does not yield a
different word (i.e., a string of sounds with a different lexical meaning). 2
The phonemes in a language differ from one another along a finite
number of phonetic dimensions, such as degree of voicing, degree of
noisiness, degree of nasality, degree of openness, degree of backness,
degree of rounding, etc. Each phonetic dimension, in turn, is subdivided
into a small number (two to four) of phonologically functional categories,
such as voiced/voiceless, (half)closed/(half)open, front/central/back, etc.
Phonetic dimensions generally have multiple acoustical correlates. For
instance, degree of voicing correlates with a multitude of acoustic cues
such as voice onset time, duration of preceding vowel, steepness of
intensity decay and of formant bends in preceding vowel, duration of
intervocalic (near) silence, duration and intensity of noise burst, steepness
of intensity attack and formant bends of following vowel. These acoustic
properties typically co-vary in preferred patterns, but may be manipulated

38 Vincent J. van Heuven
independently through speech synthesis. When non-typical (‘conflicting’)
combinations of parameter values are generated in the laboratory, some
cues prove to be more influential than others; so-called ‘cue trading
relationships’ have been established for many phonemic contrasts. In
Dutch, for instance, vowel quality (acoustically defined by F1 and F2, i.e.,
the centre frequencies of the lowest two resonances in the vocal tract) and
vowel duration were found to be equally influential in cuing the tense/laxcontrast
between /�� and /�/: a duller vowel quality (lower F1 and F2values),
normally cuing /�/ could be compensated for by increasing the
duration of the vowel so that native listeners still perceive /a �/ (and vice
versa, van Heuven, 1986).
Categorization of sounds may proceed along several possible lines.
First, many differences between sounds are simply too small to be heard at
all: these are subliminal. The scientific discipline of psycho-acoustics
provides a huge literature on precisely what differences between sounds
can and cannot be heard with the naked ear. Moreover, research has shown
that the human hearing mechanism (and that of mammals in general) has
developed specific sensitivities to certain differences between sounds and is
relatively deaf to others. These predilections have been shown to be present
at birth (probably even in utero), and need not be acquired through
learning. However, human categorization of sound is further shaped by
exposure to language. As age progresses from infancy to adulthood, sound
differences that were still above threshold shortly after birth quickly lose
their distinctivity. An important concept in this context is the notion of
categorical perception. This notion is best explained procedurally in terms
of a laboratory experiment.
Imagine a minimal word pair such as English back ~ pack. One
important difference between these two tokens is that the onset of voicing
in back is more or less coincident with the plosive release, whilst the voice
onset in pack does not start until some 50 ms after the release. It is not too
difficult in the laboratory to create a series of exemplars by interpolating
the voice onset time of a prototypical back (0-ms delay) and that of a
prototypical pack (70-ms delay) in steps of, say, 10 ms, so that we now
have an 8-step continuum ranging over 0, 10, 20, 30, 40, 50, 60, and 70 ms.
These eight exemplars are shuffled in random order and played to an
audience of native English listeners for identification as either back or pack
(forced choice). The 0-ms voice delay token will naturally come out with
exclusively back-responses (0% pack); the 70-ms token will have 100%
pack-responses. But what results will be obtained for the intermediate

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 39
exemplars? If the 10-ms changes in voice delay are perceived continuously,
one would predict a constant, gradual increase in %-pack responses for
each 10-ms increment in the delay. I.e., when the stimulus increment (from
left to right) is plotted against the response increment (from bottom to top),
the psychometric function (the line that captures the stimulus-response
relationship) is essentially a straight line (open symbols in Figure 1B). The
typical outcome of experiments with voiced/voiceless continua, however, is
non-continuous. For the first part of the continuum all exemplars are
perceived as back-tokens, the rightmost two or three exemplars are nearunanimously
perceived as pack. Only for one or two exemplars in the
middle of the continuum do we observe uncertainty on the part of the
listener: here the distribution of responses is more or less ambiguous
between back and pack. The psychometric function for this so-called
categorical perception is sigmoid, i.e., has the shape of an S (big solid
symbols in Figure 1B). In the idealized case of perfect categorical
perception we would, in fact, expect to see a step-function jumping
abruptly from (almost) 0 to (almost) 100% pack-responses somewhere
along the continuum (thin black line with small solid symbols in Figure
1B).
The category boundary (at 35-ms VOT in Figure 1B) is defined as the
(interpolated) point along the stimulus axis where the distribution of
responses is completely ambiguous, i.e., 50-50%. For a well-defined crossover
from one category to the other there should be a point along the
stimulus axis where 75% of the responses agree on one category, and a
second point where there is 75%-agreement on the other category. The
uncertainty margin is defined in absolute terms as the distance along the
stimulus axis between the two 75%-points; equivalent relative measures
can be derived from the steepness of the psychometric function (e.g. the
slope coefficient or the standard deviation of the cumulative normal
distribution fitted to the data points).

40 Vincent J. van Heuven
Figure 1. Panel A. Hypothetical discrimination function for physically same and
different pairs of stimuli (one-step difference) reflecting categorical
perception. Panel B. Illustration of continuous (open squares) versus
categorical (big solid squares) perception in the identification and
discrimination paradigm. The thin line with small squares represents the
ideal step function that should be obtained when categorical perception is
absolute. Category boundary and uncertainty margin are indicated
(further, see text).
Although a pronounced sigmoid function (such as the one drawn in Figure
1B) is a clear sign of categorical perception, researchers have always been
reluctant to consider it definitive proof. Listeners, when forced to, tend to
split any continuum down the middle. For a continuum to be perceived
categorically, therefore, two conditions should be met:

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 41
- results of an identification experiment should show a clear sigmoid
function, and
- the discrimination function should show a local peak for stimuli
straddling the category boundary.
The discrimination function is determined in a separate experiment in
which either (i) identical or (ii) adjacent tokens along the stimulus
continuum are presented pair-wise. Listeners then decide for each pair
whether the two tokens are ‘same’ or ‘different’. Two kinds of error may
occur in a discrimination task:
- a physically different pair may be heard as ‘same’, and
- a pair of identical tokens may be called ‘different’.
The results of a discrimination task are best expressed as the percentage of
correct decisions obtained for a ‘different’ stimulus pair minus the
percentage of errors for ‘same’ pairs constructed from these stimuli (the
latter percentage is often called the response bias). In the case of true
categorical perception the discrimination scores show a pronounced peak
for the stimulus pair straddling the category boundary, whilst all other pairs
are discriminated at or only little above chance level (see panel A in Figure
1). Physically different sounds that fall in the same perceptual category are
hard to discriminate. In the case of continuous perception, there is no local
peak in the discrimination function.
1.2. Categorical nature of intonational contrasts
By intonation or speech melody we mean the pattern of rises and falls in
the time-course of the pitch of spoken sentences. Melodic patterns in
speech vary systematically across languages, and even within languages
across dialects. The cross-linguistic differences can be parameterized and
described in much the same way as has been done for the segmentals in
language: a set of distinctive features defines an inventory of abstract units,
which can be organized in higher-order units subject to wellformedness
constraints. Moreover, intonational contrasts are used to perform
grammatical functions that can also be expressed by lexico-syntactic
means, such as turning statements into questions, and putting constituents
in focus. For these reasons it has become widely accepted that intonation is

42 Vincent J. van Heuven
part of the linguistic system (Ladd, 1996: 8). Yet, there have always been
adherents of the view that speech melody should be considered as
something outside the realm of linguistics proper, i.e., that intonation is a
paralinguistic phenomenon at best, to be treated on a par with the
expression of attitudes or emotions. Typically, the communication of
emotions (such as anger, fear, joy, surprise) or of attitudes (such as
sarcasm) is non-categorical: the speaker shows himself more or less angry,
fearful, or sarcastic in a continuous, gradient fashion.
A relatively recent insight, therefore, is that a division should be made
in melodic phenomena occurring in speech between linguistic versus
paralinguistic contrasts. Obviously, only the former but not the latter type
of phenomena should be described by the grammar and explained by
linguistic theory. This, however, begs the question how the difference can
be made between linguistic and paralinguistic phenomena within the realm
of speech melody. 3 Ladd & Morton (1997) were the first to suggest that the
traditional diagnostic for categorical perception should be applicable to
intonational categories in much the same ways as it works for segmental
contrasts. Only if a peak in the discrimination function is found for adjacent
members on a tone continuum straddling a boundary between tonal
categories, are the categories part of the linguistic system, i.e., phonological
categories. If no categorical perception of the tone categories can be
established, the categories are ‘just’ the extremes of a paralinguistic or
phonetic tonal continuum. Ladd & Morton tested the traditional diagnostic
on a tone continuum between normal and emphatic accent in English and
noted that it failed. This – to me – indicates that the contrast is not part of
the phonology of English.
Remijsen & van Heuven (1999, 2003) tested the traditional diagnostic
on a tone continuum between ‘L%’ and ‘H%’ in Dutch, and showed that
indeed there was a discrimination peak for adjacent members along the
continuum straddling the boundary – indicating that the ‘L%’ and ‘H%’
categories are part of the phonology of Dutch. At the same time, however,
we had to take recourse to listener-individual normalization of the category
boundary, a complication that is not generally needed when dealing with
contrasts in the segmental phonology. 4
Van Heuven & Kirsner (2002) suggested that the relatively weak
categorical effects in Remijsen & van Heuven could have been the result of
an incorrect subdivision of the ‘L%’ to ‘H%’ tone range. Van Heuven &
Kirsner (2002) showed that Dutch listeners were perfectly able to
categorize a range of final pitches between low and high in terms of three

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 43
categories, functionally denoted as command intonation, continuation, and
question. However, we did not run the full diagnostic involving both
identification and discrimination procedures. Moreover, Van Heuven &
Kirsner forced their listeners to choose between three response alternatives,
viz. command, conditional and question. Although the extremes of the
range, i.e. command versus question are unchallenged categories, it may
well be the case that the conditional is not necessarily distinct from the
question type. After all, in the grammar developed by ‘t Hart, Collier &
Cohen (1990) any type of non-low terminal pitch falls into the same
category, indicating non-finality. It occurred to us that we should take the
precaution to run the experiment several times, using different response
alternatives, such that two separate binary (‘command’ ~ ‘no command’
and ‘question ~ ‘no question’) response sets as well as the ternary response
set (‘command’ ~ ‘conditional’ ~ ‘question’) were used by the same set of
listeners. If the intermediate ‘conditional’ response category does constitute
a clearly defined notion in the listeners’ minds, the binary and ternary
divisions of the stimulus range should converge on the category boundaries.
The present paper seeks to remedy the infelicities of Van Heuven &
Kirsner (2002). However, before I deal with the experiments, it is necessary
to introduce the inventory of the domain-final boundary configurations that
can be found in Dutch.
1.3. Dutch domain-final boundary tones
Over the past decades a major research effort has been spent on the formal
description of the sentence melody of Dutch. In the present paper we
concentrate on one small part of the intonation system of Dutch: the options
that are available to the speaker to terminate an intonation phrase. It has
become customary to model the intonation system of a language as a
hierarchically organized structure in which the tonal primitives (or ‘atoms’)
are combined into tonal configurations, which in turn combine into
intonation phrases. One or more of such intonation phrases are combined
into an utterance, which may combine with other utterances to form a
prosodic paragraph. The intonation phrase (henceforth IP), then, is situated
roughly in the middle of the prosodic hierarchy. Note that a short utterance
may consist of just one IP. An IP is characterized as a stretch of speech
between two IP boundaries, i.e., a break in the segment string that is
signaled by either a pause (physical interruption of the sound stream), pre-

44 Vincent J. van Heuven
boundary lengthening and/or by a boundary-marking tone. If the boundary
is sentence medial, then yet another IP must follow in order to finish the
utterance.
The first explicit and experimentally verified grammar of Dutch
intonation was developed at the Institute for Perception Research at
Eindhoven (‘t Hart et al., 1990; Rietveld & van Heuven, 2001: 263-270).
This grammar models the sentence melody of Dutch as a system of two
gently declining reference lines, nominally 6 semitones (half an octave)
apart, between which the pitch rises and falls in a limited number of
patterns. The grammar provides for three different ways in which an IP
may be terminated: (i) on the low reference line (‘0’), (ii) on the high
reference line (‘∅’), or (iii) by executing a steep pitch rise (‘2’). Although
the grammar is not completely explicit on this point, it appears that the
offset of rise ‘2’ may exceed the level of the high reference line,
specifically when the rise starts at the high reference line. The grammar
then allows IPs to end at three different pitches: low, high, and extra high.
A more recent account of Dutch intonation is given by Gussenhoven and
co-workers (Gussenhoven, Rietveld & Terken, 1999; Rietveld & van
Heuven, 2001: 270-277). This model is constructed along the principles
adopted by autosegmental intonologists, in which a sentence melody is
basically a sequence of tonal targets of two types: ‘H’ (high) and ‘L’ (low).
The ToDI system (Transcription of Dutch Intonation), which is an
inventory of tonal configurations for surface-level transcriptions of Dutch
sentence melodies using the autosegmental H/L notation format, provides
three symbols for marking IP boundaries: (i) ‘L%’, i.e., the final pitch
target extends below the baseline, (ii) ‘%’, i.e., the absence of a tonal IP
boundary marker, and (iii) ‘H%’, i.e., the final pitch is higher than the
preceding pitch. 5 For details of the ToDI transcription system I refer to the
ToDI website (www.lands.kun.nl/todi) or to Rietveld & van Heuven (2001:
399-401).
Remijsen & van Heuven (1999, 2003) report an experiment which
sought to establish the perceptual boundary between sentence-final
statement and question intonation. They did this by varying the pitch
configuration on the utterance-final syllable of the verb-less phrase De
Dennenlaan(?) ‘Pine Lane(?)’ between a fall and a steep rise in eleven
perceptually equal steps. Listeners were then asked to decide for each of the
eleven pitch patterns whether they perceived it as a statement or a question.
At the time we tacitly assumed that the continuum spanned just two
pragmatic categories, i.e. statement versus question, and that there was no

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 45
relevant intermediate category that could be interpreted as ‘non-finality’. In
fact, Kirsner & van Heuven (1996) suggested a single abstract meaning for
the non-low tonal category: ‘appeal (by the speaker to the hearer)’, asking
for the hearer’s continued attention or for a verbal response to a question or
a non-verbal compliance with a request. However, Caspers (1998)
suggested that there is a functional difference between the non-tonal
boundary (‘%’) following an earlier ‘H*’ target and the high boundary
(‘H%’) following an earlier ‘H*’. She synthesized stimuli in which the
terminal pitch after the accent-marking ‘H*’ was followed by either ‘H%’
(where the final pitch was raised further) or just % (where the pitch
remained high but level after the accent). Her results indicate that listeners
unequivocally expect the speaker to continue after the ‘H* ... %’
configuration, in contradistinction to the ‘H* ... H%’ pattern, for which the
responses were equally divided between ‘same speaker will continue’ and
‘interlocutor will take over (with a response)’.
Note that the ‘%’ tone-less boundary as studied by Caspers is found
only after a preceding H* accent. Strictly speaking, then, the ‘%’ boundary
cannot be used as an intermediate category in between ‘L%’ and ‘H%’
when the preceding pitch is low. After ‘L’, any rise in pitch, whether strong
or intermediate, is a perceptually relevant change in pitch, which must be
coded by an ‘H%’ target. On the other hand, this formal constraint is in the
way of an attractive generalization which would allow us to view the high
level pitch (‘H* ... %’) pattern as a surface realization of the ‘H*L...%’
pattern from which the L target has been deleted – in much the same way as
was suggested by Haan (2002) in order to account for the functional
similarity between the ‘H*...H%’ and the ‘H*L…H%’ interrogative
patterns, as exemplified in Figure 2.
Figure 2. Underlying tonal shape (dotted) and surface realization after ‘L’-deletion
(solid) of an ‘H*L … H%’ sequence.
There seems to be a mismatch between the functions expressed by Caspers’
‘%’ and ‘H%’ after ‘H*’. If we assume an iconic relationship between the

46 Vincent J. van Heuven
terminal pitch of the utterance and the degree of submissiveness of the
speaker towards the hearer, then we would reason that ‘H%’ should make
more of an appeal to the hearer (expressing greater submissiveness) than
just ‘%’. On the other hand, answering a question seems a bigger favor on
the part of the hearer than merely waiting for the speaker to continue the
utterance. It could be the case, of course, that even the highest terminal
pitches used by Caspers were not high enough to elicit unambiguous ‘other
speaker will take over’ (i.e. ‘question’) responses. Also, it is unclear if the
unambiguous ‘same speaker will continue’ response crucially depends on a
flat stretch of high declination (as is the case after an ‘H*’ accent) or if any
terminal pitch of intermediate height would yield the same response.
In Caspers’ analysis the ‘%’ boundary – and arguably an ‘L … H%’
sequence with a moderately high terminal pitch – unambiguously signals
continuation. This category would then be expected to be firmly
represented in the listener’s cognitive system. Varying the terminal pitch
from low to extremely high should then elicit two well-defined categories:
(i) unambiguous statement for low pitches, (ii) unambiguous continuations
for intermediate terminal pitches, and (iii) a poorly defined or non-unique
interrogative category, which is also compatible with a continuation
reading.
At this time, then, we do not know whether two or three formal tone
categories should be postulated in IP-final position. It seems that the status
of ‘L%’ as a linguistic category is unchallenged but the non-low part of the
IP-final tone range is very much a matter of debate. Does the non-low part
of the range form a continuum expressing lesser or greater appeal by the
speaker in a paralinguistic manner, or should this part of the range be split
into two discrete phonological categories, each expressing a distinct
meaning of its own (i.e. ‘continuation’ ~ ‘question’, or – even worse – into
two categories of which one is specific for ‘continuation’ and the other
underspecified and compatible with both ‘question’ and ‘continuation’?
These meanings, and a possible way of testing the categorical nature of
tonal contrasts expressing them, are the topic of the next section.
1.4. Clause typing
Dutch, like any other language, has lexico-syntactic means to express a
range of clause types, such as statement, command, exclamation and
question. Although the lexico-syntactic means are generally adequate and

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 47
sufficient to express the speaker’s pragmatic intention to the hearer, several
– if not all – clause types are supported by prosodic means, specifically by
appropriate intonation patterns. In fact, exceptional situations may arise
where there is no lexico-syntactic differentiation between the clause types,
and where the speaker’s intention can only be recovered from melodic
cues. For the purposes of the present experiment we have looked for a
situation in which the three prosodic categories may serve as the only cue to
a ternary choice among clause types, so that prosody will be exploited to
the utmost, and the listener’s choice will not be co-determined by lexical
and/or syntactic cues. Such a situation may be obtained in a V1 sentence,
where the finite verb has been moved into the sentence-initial position. 6 In
the sentence Neemt u de trein naar Wageningen ‘Take you the train to
Wageningen’ the lexico-syntactic information is compatible with at least
three interpretations: 7
- A polite imperative (Kirsner, van Heuven & Caspers, 1998)
- A conditional clause similar in meaning to ‘If you take the train to
Wageningen ...’
- A yes/no question ‘Do you take the train to Wageningen?’
Which of the three readings is intended by the speaker, is expressed
through prosody only. In setting up the experiment we assumed that there is
no principal difference in the speech melody between a statement and a
command in Dutch. 8 Using a range of terminal pitch patterns on the single
phrase Neemt u de trein naar Wageningen, we can determine the category
boundaries between command (for statement), conditional (for
continuation), and question without any interfering differences in lexicosyntactic
structure.
We may conclude this introduction by summarizing the research
questions that we will address:
1. Are the domain-final boundaries ‘L%’ ~ ‘%’ ~ ‘H%’ contiguous
categories along a single tonal dimension?
2. Is there a one-to-one correspondence between ‘L%’ and ‘command’,
‘%’ and ‘conditional’, and ‘H%’ and ‘question’?
3. Where are the category boundaries – if any – along the continuum
between (i) ‘L%’ and ‘%’ and (ii) between ‘%’ and ‘H%’?
4. Are the category boundaries at the same positions along the stimulus
range irrespective of the binary versus ternary response mode?

48 Vincent J. van Heuven
5. Are both boundaries truly categorical in the sense that there are
discrimination peaks for adjacent stimulus pairs straddling the category
boundaries?
2. Methods
2.1. Stimuli
A male native speaker of standard Dutch read the sentence Neemt u de trein
naar WAgeningen? with a single ‘H*L’ accent on the first syllable of
Wageningen. The utterance was recorded onto digital audio tape (DAT)
using a Sennheiser MKH 416 unidirectional condenser microphone,
transferred to computer disk (16 kHz, 16 bits) and digitally processed using
the Praat speech processing software (Boersma & Weenink, 1996; Boersma
& van Heuven, 2001). The intonation pattern of the utterance was stylized
by hand as a sequence of straight lines in the ERB x linear time
representation. Nine intonationally different versions were then generated
using the PSOLA analysis-resynthesis technique (e.g. Moulines & Verhelst,
1995; Rietveld & van Heuven, 2001: 379-380) implemented in the Praat
software. The nine versions were identical up to and including the ‘H*L’
configuration on Wageningen. From that point onwards the nine versions
diverged into two falls and seven rises. The terminal frequencies of the nine
versions were chosen to be perceptually equidistant, i.e., the difference
between any two adjacent terminal frequencies was equal in terms of the
ERB scale. 9 The terminal pitch of version 1 equaled 80 Hz, the increment
in the terminal frequency for each following version was 0,25 ERB. The
nine pitch patterns are shown in Figure 3.

80
60
160
120
80
Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 49
neemt u de trein naar WA ge ni ngen
0 0.5
1
1.5
Time (s)
Figure 3. Steps 1 through 9 along resynthesized continuum differing in terminal F0
by 0,25 ERB increments. Intensity contour (dB) and segmentation (by
syllables) are indicated.
2.2. Tasks and experimental procedures
For the discrimination task, which was the first task imposed on the
subjects, we followed Ladd and Morton (1997) in using the AX
discrimination paradigm. Stimuli were presented in pairs that were either
the same or one step apart on the continuum. In the latter case, the second
can be higher or lower than the first (hereafter AB and BA, respectively).
The eight AB stimulus types ran from pair {1,2} to {8,9}; the eight
corresponding BA types from {2,1} to {9,8}. This yielded 9 identical pairs
and 2 x 8 = 16 different pairs, which occurred in random order, yielding a
set of 25 trials in all, which was presented to each listener four times in
different random orders, preceded by five practice trials. Stimuli within
pairs were separated by a 500-ms silence, the pause between pairs was
3000 ms. A short warning tone was sounded after every tenth trial.
For the identification task listeners responded to individual stimuli from
the 9-step continuum by classifying each either in terms of a binary or a
ternary choice:
1. ‘Command’ ~ ‘no command’. In one task the listeners were instructed
to decide for each stimulus whether they interpreted it as a command or
not.
2. ‘Question’ ~ ‘no question’. An alternative task involved the decision
whether the stimulus sounded like a question or not.

50 Vincent J. van Heuven
3. ‘Command’ ~ ‘condition’ ~ ‘question’. The third task was identical to
the task imposed in van Heuven & Kirsner (2002).
Half of the listeners first performed task (1), the other half of the listeners
began with task (2). Task (3) was always the last identification procedure in
the array of tests. For each task, the set of nine stimuli were presented five
times to each listener, in different random orders, and preceded by five
practice items, yielding sets of 50 identification stimuli per task.
Twenty native Dutch listeners, ten males and ten females, took part in
the experiment on a voluntary basis. Participants were university students
or members of their families. None of them reported any perceptual
deficiencies.
The experiments were run with small groups of subjects, who listened to
the stimuli at a comfortable loudness level over Quad ESL-63 electrostatic
loudspeakers, while seated in a sound-treated lecture room. Subjects
marked their responses on printed answer sheets provided to them, always
taking the discrimination task first and the identification tasks last.
3. Results
3.1. Identification
Figures 4 and 5 present the results obtained in the binary identification
tasks, i.e., the forced choice between ‘command’ ~ ‘no command’ (Figure
4) and between ‘question’ ~ ‘no question’ (Figure 5).

Indentifications "command" (%)
Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 51
Figure 4. Percent ‘command’ responses as a function of stimulus step (terminal F0
increments in 0.25 ERB steps) in a binary identification task (‘command’
~ ’no command’).
The psychometric function for the ‘command’ responses is very steep. The
category boundary between ‘command’ and ‘no command’ is located at a
step size of 2.7, and the margin of uncertainty runs between 2.2 and 3.7,
i.e., a cross-over from 75% to 25% ‘command’ responses is effected by an
increase in the terminal pitch of the stimulus of 1.5 step (i.e., 0.37 ERB).
Indentifications "question" (%)
100
80
60
40
20
0
1
100
80
60
40
20
0
1
2
2
3
3
Stimulus step
4
4
5
5
6
6
Stimulus step
7
7
8
8
Figure 5. Percent ‘question’ responses as a function of stimulus step (terminal F0
increments in 0.25 ERB steps) in a binary identification task (‘question’
~ ’no question’
9
9

52 Vincent J. van Heuven
A complete cross-over is also found for the ‘question’ ~ ‘no question’ task.
The category boundary finds itself at a stimulus value of 3.6, whilst the
margin of uncertainty runs between 2.3 and 4.9, i.e., an interval of 2.6
increments of 0.25 ERB. We may note that the category boundaries in the
‘command’ and the ‘question’ tasks do not coincide, but are separated
along the stimulus axis by almost a complete step: 2.7 versus 3.6 or 0.9
step. Note, once more, that none of the subjects had been alerted to the
possible existence of an intermediate category between ‘command’ and
‘question’. Therefore, the emergence of the interval between the
‘command’ and the ‘question’ boundaries might be taken in justification of
such an intermediate category.
Let us now turn to the results of the ternary identification task in which
all the listeners who had already responded to the stimuli were now
required to classify the nine stimulus types as either ‘command’,
‘conditional subclause’ or ‘question’. These results are shown in Figure 6.
Identification (%)
100
80
60
40
20
0
1
2
3
4
5
6
Stimulus step
7
8
9
'command'
'continuation'
'question'
Figure 6. Ternary identification of stimuli as ‘command’, ‘conditional clause’ or
‘question’. Category boundaries are indicated.
The boundary between ‘command’ and the ‘continuation’ categories is at
2.8; this is hardly different than the ‘command’ ~ ’no command’ boundary
that was found in the binary response task. This, then, would seem to be a
very robust boundary, showing that at least ‘command’ intonation has welldefined
linguistic status. The boundary between ‘continuation’ and
‘question’ is less clearly defined. Also, the maximum scores in these two

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 53
categories are around 80% rather than 90% or more. Although there is no
ambiguity in the listeners’ minds whether a stimulus is a command or
something else, the choice between ‘continuation’ and ‘question’ seems
more ambiguous leaving room for a minority response in the order of 20%.
This would indicate to us that we are dealing here with a continuum rather
than with a dichotomy. Finally, we may note that the (soft) category
boundary between ‘continuation’ and ‘question’ is located at a stimulus
value of 7.2. The boundary, then, that sets off ‘question’ from ‘no question’
responses proves very unstable: there is a shift from the binary response
task (3.6) to the ternary task (7.2) of no less than 3.6 points along the
stimulus continuum.
It would seem, then, that the ‘command’ category is highly stable and
well-established in the minds of the listeners. The ‘question’ boundary,
however, is rather poorly defined, as a result of several circumstances. The
cross-over points for the ‘question’ category of individual listeners vary
over a wide range of stimulus values, i.e., between 2.2 and 8.5 step number,
with a fairly even spread of values in between these extremes. Moreover,
for two listeners no cross-over to the ‘question’ category could be found at
all; here the listeners never gave the ‘question’ response in more than 75%.
Also, some listeners have extremely sharp cross-overs to the ‘question’
category, but others show large margins of uncertainty.
3.2. Discrimination
Figure 7 presents the mean percentage of successfully discriminated stimuli
that were actually different (hereafter ‘hits’), and the percentage of false
alarms, i.e. ‘different’ responses to (identical) AA stimuli. The false-alarm
rate is roughly 20% across the entire stimulus continuum. This value can be
seen as a bias for responding ‘different’. Generally, an increment of 0.25
ERB is discriminated above the 20% bias level, with the exception of the
difference between stimulus steps 5 and 6. The discrimination function
shows two local peaks. The first one is very large, and is located between
stimulus steps 2 and 3. This peak obviously coincides with the stable
category boundary found between ‘command’ and the non-command
responses (whether binary or ternary). A much smaller second
discrimination peak may be observed between stimulus steps 6 and 7,
which location may well reflect the rather poorly defined category
boundary between ‘continuation’ and ‘question’.

54 Vincent J. van Heuven
"Different" judgments (%)
100
80
60
40
20
0
1 2 3 4 5 6 7 8 9
1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9
Stimulus step
Figure 7. Percent ‘different’ judgments to nine identical stimulus pairs (false
alarms) and eight pairs differing by one step (hits).
4. Conclusions and discussion
"same" pair
"diff" pair
Let us now try to formulate answers to the research questions that we asked
in section 2. The first two questions, which I will attempt to answer
together, asked whether the domain-final boundary tones are contiguous
categories along a single tonal dimension, and map onto the command,
continuation and question meaning in a one-to-one fashion. The results of
our experiments clearly indicate that this is indeed the case. Our listeners
had no difficulty in using the three response alternatives provided to them.
When the terminal pitch was lower than the preceding pivot point in the
contour the responses were almost unanimously for ‘command’. When the
IP-final pitch was higher than the preceding pivot point, the incidence of
‘continuation’ responses increased up to and including step 4, and
decreased for higher terminal pitches which were more readily identified as
questions as the terminal pitch was higher. Although there was always
some ambiguity between the ‘continuation’ and ‘question’ alternatives, the
results clearly indicate that ‘continuation’ is signaled by moderate final
pitch, and question by (extra) high pitch.
The latter finding corresponds with our suggestion that asking a
question involves a higher degree of appeal by the speaker to hearer than

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 55
asking the listener’s continued attention. We may also note that our result
clashes with Caspers (1998). She found that the intermediate final pitch (or
high level pitch in her experiment) was unambiguously identified as
continuation; extra high final pitch ambiguously coded either continuation
or question. Comparison of Caspers’ and our own results is hazardous since
the utterance-final tone configurations differ, not so much at the underlying
tone level, but at the surface. It seems to me that the discrepancy between
Caspers’ and our own findings can be resolved if we accept the possibility
that Caspers’ extra high terminal pitch was simply not high enough to elicit
the 80% ‘question’ responses that we got in our experiment.
The results so far concur with van Heuven & Kirsner (2002). However,
we may now go on to consider the third, fourth and fifth question, which
asked where the category boundaries are located along the final pitch
continuum between ‘L%, ‘%’ and ‘H’, in the binary and ternary response
tasks, and to what extent the boundaries coincide with a peak in the
discrimination function.
The results obtained in the binary (‘command’ ~ ‘no command’) and
ternary (‘command’ ~ ‘continuation’ ~ ‘question’) identification tasks are
virtually the same, yielding the same location of the boundary (at step 2.7)
separating the ‘command’ category from the rest of the stimulus
continuum. However, a very unstable boundary is found in the binary
‘question’ ~ ‘no question’ task (at step 3.6), which is reflected in the poorly
defined boundary separating the ‘continuation’ and ‘question’ categories in
the ternary response task (at step 7.2). Moreover, we have seen that the
category boundary between ‘command’ and ‘no command’ coincides with
a huge peak in the discrimination function. Although there is a modest local
maximum in the discrimination function that may be associated with a
boundary between ‘continuation’ and ‘question’, this peak is not very
convincing.
I take these findings as evidence that there is a linguistic, or
phonological, categorization of the IP-final boundary tone continuum in
just two types, which is best characterized as low and non-low. The low
boundary tone signals dominance or superiority on the part of the speaker.
This is the boundary tone that is suited for issuing statements and
commands. The non-low boundary tone signals subservience of the speaker
to the hearer; the speaker appeals to the hearer for his continued attention or
for an answer to a question.
The non-low part of the boundary opposition, however, represents a
gradient, paralinguistic continuum between a moderate appeal (asking for

56 Vincent J. van Heuven
the hearer’s continued attention) and a stronger appeal (asking the hearer
for a verbal reply to a question). Here the lower terminal pitches are
associated with weaker degrees of appeal (or subservience), and the higher
levels with strong appeal, but in a continuous, gradient, non-phonological
manner.
Our results indicate that earlier findings reported by Remijsen & van
Heuven (1999, 2003) are to be viewed with caution. We now know that the
proper task to be imposed on listeners should not be to decide whether the
stimulus is a statement (or a command) versus a question. If binary
response alternatives are required, then the categories should be ‘statement’
versus ‘no statement’ but a better procedure would be to ask the listener to
respond by choosing from three categories: ‘statement’ (equivalent to
‘command’ in our experiments ~ ‘continuation’ ~ ‘question’. Had such
precautions been taken by Remijsen & van Heuven, their category
boundary would have been much better defined with less listener-individual
variation.
Methodologically, we argue that the classical identification-cumdiscrimination
paradigm is a useful diagnostic tool in intonation research
which allows linguists to decide experimentally whether a melodic contrast
is categorical and therefore part of the phonology, or continuously gradient
and therefore phonetic or even paralinguistic.
Notes
1 The experiments reported in this chapter were run by Susanne Strik and Josien
Klink in partial fulfillment of the course requirements for the Experimental
Phonetics Seminar taught by the Linguistics Programme at University of
Leiden.
2 This commutation procedure is best viewed as a mental experiment; when the
exchange is implemented through actual digital tape splicing, the result is more
often than not an uninterpretable stream of sound.
3 The nature of the distinction between intonational categories is problematic for
a further reason: inter-listener agreement on the identity of intonational events
is low (Pitrelli et al., 1994), particularly in comparison with the self-evident
consensus on segmental distinctions. This lack of consistency has lead Taylor
(1998) to reject a basic principle of (intonational) phonology, namely its
categorical nature. With respect to methodology, researchers tend to act as
expert listeners, linking contours that sound distinct to pragmatic meaning in an

Boundary Tones in Dutch: Phonetic or Phonological Contrasts? 57
intuitive fashion. Accordingly, inter-researcher agreement may be low, too (e.g.
Caspers, 1998).
4 Nevertheless, large between-listener variability has been reported, for instance, in
the cuing of the voiced/voiceless contrast by the duration of the pre-burst silent
interval: the boundary was at 70 ms for subject #1 and over 100 ms for subject #7
(Slis & Cohen, 1969). These results are commented on by Nooteboom & Cohen
(1976: 84) as follows: ‘Although the cross-over from /d/ to /t/ proceeds rather
gradually when averaged over all listeners, the boundary is quite sharply defined for
individual listeners’ (my translation, VH).
5 The ‘%’ sign following the tone letter (as in ‘L%’, ‘H%’) denotes a domainfinal
boundary; domain-initial boundaries are coded by the ‘%’ sign preceding
a tone letter (as in ‘%L’, ‘%H’). A ‘%’ sign unaccompanied by a tone letter
may only occur in domain-final positions, where it is phonetically coded by a
physical pause and/or pre-boundary lengthening only.
6 It has been argued by structuralists at least as far back as Merckens (1960) that
V1 (‘verb first’) is directly opposed to V2 ('verb second') in signaling, for
example, ‘non-assertion’ rather than ‘assertion’, since neither a command nor a
question nor a condition expresses an ongoing state of affairs.
7 A sequence like Neemt u de trein naar Wageningen might in addition be
interpretable as a topic-drop-sentence (e.g. [Dan/Daar] neemt u de trein naar
Wageningen ‘[Then/There] you take the train to Wageningen’, analogous to
Doen we! ‘We'll do [it]’ or Weet ik! ‘[That] I know’. Although this added
interpretation (with a ‘deleted’ element) is theoretically possible, we believe
that it was highly unlikely under the controlled conditions of the experiment.
Furthermore, none of the experimental subjects volunteered the information
that we had forgotten such an extra interpretation.
8 This position does not exclude the possibility that statement and imperative are
subtly different in their paralinguistic use of prosody. For instance, the overall
pitch of the imperative may be lower, and it may be said with greater loudness
and larger/higher pitch excursions on the accented syllables. This does not
invalidate our claim that both statements and imperatives are coded by the ‘L%’
terminal boundary.
9 The ERB scale (Equivalent Rectangular Bandwidth) is currently held to be the
most satisfactory psychophysical conversion for pitch intervals in human
speech (Hermes & van Gestel, 1991; Ladd & Terken, 1995). The conversion
from Hertz (f) to ERB (E) is achieved by a simple formula: E = 16.6 * log (1 + f
/ 165.4).

58 Vincent J. van Heuven
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International, 5: 341-347.
Boersma, P. and Weenink, D. (1996). Praat, a System for Doing Phonetics by
Computer. Report of the Institute of Phonetic Sciences
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Caspers, J. (1998). ‘Who’s Next? The Melodic Marking of Question vs.
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Gussenhoven, C., Rietveld, T. and Terken, J.M.B. (1999). Transcription of
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Heuven, V.J. van and Kirsner, R.S. (2002). Interaction of tone and particle in
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Kirsner, R.S. and Heuven, V.J. van (1996). Boundary Tones and the Semantics
of the Dutch Final Particles hè, hoor, zeg and joh. In: M. den
Dikken, C. Cremers, eds., Linguistics in the Netherlands 1996,
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Kirsner, R.S., Heuven, V.J. van, and Caspers, J. (1998). From Request to
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Research, University of Edinburgh.

The Position of Frisian in the Germanic Language
Area
Charlotte Gooskens and Wilbert Heeringa
1. Introduction
Among the Germanic varieties the Frisian varieties in the Dutch province
of Friesland have their own position. The Frisians are proud of their
language and more than 350,000 inhabitants of the province of Friesland
speak Frisian every day. Heeringa (2004) shows that among the dialects in
the Dutch language area the Frisian varieties are most distant with respect
to standard Dutch. This may justify the fact that Frisian is recognized as a
second official language in the Netherlands. In addition to Frisian, in some
towns and on some islands a mixed variety is used which is an intermediate
form between Frisian and Dutch. The variety spoken in the Frisian towns is
known as Town Frisian 1 .
The Frisian language has existed for more than 2000 years. Genetically
the Frisian dialects are most closely related to the English language.
However, historical events have caused the English and the Frisian
language to diverge, while Dutch and Frisian have converged. The
linguistic distance to the other Germanic languages has also altered in the
course of history due to different degrees of linguistic contact. As a result
traditional genetic trees do not give an up-to-date representation of the
distance between the modern Germanic languages.
In the present investigation we measured linguistic distances between
Frisian and the other Germanic languages in order to get an impression of
the effect of genetic relationship and language contact for the position of
the modern Frisian language on the Germanic language map. We included
six Frisian varieties and one Town Frisian variety in the investigation.
Furthermore, eight Germanic standard languages were taken into account.
Using this material, we firstly wished to obtain a hierarchical classification
of the Germanic varieties. From this classification the position of (Town)

62 Charlotte Gooskens and Wilbert Heeringa
Frisian became clear. Secondly, we ranked all varieties with respect to each
of the standard Germanic languages as well as to (Town) Frisian. The
rankings showed the position of (Town) Frisian with respect to the standard
languages and the position of the standard languages with respect to
(Town) Frisian.
In order to obtain a classification of varieties and establish rankings, we
needed a tool that can measure linguistic distances between the varieties.
Bolognesi and Heeringa (2002) investigated the position of Sardinian
dialects with respect to different Romance languages using the Levenshtein
distance, an algorithm with which distances between word pronunciations
are calculated. In our investigation we used the same methodology.
In Section 2, we will present the traditional ideas about the genetic
relationship between the Germanic languages and discuss the relationship
between Frisian and the other Germanic languages. At the end of the
section we will discuss the expected outcome of the linguistic distance
measurements between Frisian and the other Germanic languages. In
Section 3 the data sources are described and in Section 4 the method for
measuring linguistic distances between the language varieties is presented.
The results are presented in Section 5, the discussion of which is presented
in Section 6.
2. Frisian and the Germanic languages
2.1. History and classification of the Germanic languages 2
The Germanic branch of the Indo-European languages has a large number
of speakers, approximately 450 million native speakers, partly due to the
colonization of many parts of the world. However, the number of different
languages within the Germanic group is rather limited. Depending on the
definition of what counts as a language there are about 12 different
languages. Traditionally, they are divided into three subgroups: East
Germanic (Gothic, which is no longer a living language), North Germanic
(Icelandic, Faeroese, Norwegian, Danish, and Swedish), and West
Germanic (English, German, Dutch, Afrikaans, Yiddish, and Frisian).
Some of these languages are so similar that they are only considered
independent languages because of their position as standardized languages

The Position of Frisian in the Germanic Language Area 63
spoken within the limits of a state. This goes for the languages of the
Scandinavian countries, Swedish, Danish and Norwegian, which are
mutually intelligible. Other languages consist of dialects which are in fact
so different that they are no longer mutually intelligible but are still
considered one language because of standardization. Northern and southern
German dialects are an example of this situation.
Figure 3. The genetic tree of Germanic languages.
In Figure 1, a traditional Germanic genetic tree is shown. We constructed
this tree on the basis of data in the literature. The tree gives just a rough
division, and linguistic distances should not be derived from this tree. It is
commonly assumed that the Germanic languages originate from the
southern Scandinavian and the northern German region. After the migration
of the Goths to the Balkans towards the end of the pre-Christian era, North-
West Germanic remained uniform till the 5th century AD, after which a
split between North and West Germanic occurred owing to dialectal
variation and the departure of the Anglo-Saxons from the Continent and the
colonization of Jutland.
During the Viking Age, speakers of North Germanic settled in a large
geographic area, which eventually led to the five modern languages (see
above). Of these languages, Icelandic (and to a lesser degree Faeroese),
which is based on the language of southwestern Norway where the settlers
came from, can be considered the most conservative language (Sandøy,
1994). Of the three mainland Scandinavian languages, Danish has moved

64 Charlotte Gooskens and Wilbert Heeringa
farthest away from the common Scandinavian roots due to influences from
the south.
The parentage of the West Germanic languages is less clear. Different
tribal groups representing different dialect groups spread across the area,
which eventually resulted in the modern language situation. Historically
Frisian and English both belong to the Ingwaeonic branch of the West
Germanic language group. Originally the Frisian speech community
extended from the present Danish-German border along the coast to the
French-Belgian border in the south. However, expansion from Saxons and
Franconians from the east and the south throughout the medieval period
resulted in a loss of large Frisian areas and a division into three mutually
intelligible varieties: West Frisian (spoken in the northern Dutch province
of Friesland by more than 350,000 people), East Frisian or Saterlandic
(spoken by a thousand speakers in three villages west of Bremen) and
North Frisian (spoken by less than ten thousand people on the islands on
the north-western coast of Germany).
The English language came into being as a result of immigrations of
tribal Anglo-Saxon groups from the North Sea coast during the fifth and
sixth centuries. Whereas other insular Germanic varieties are in general
rather conservative, the English insularity lacked this conservatism. English
is considered most closely related to Frisian on every linguistic level due to
their common ancestorship and to continued language contact over the
North Sea.
The German language is spoken in many European countries in a large
number of dialects and varieties, which can be divided into Low German
and High German. Yiddish, too, can be regarded as a German variety.
Dutch is mainly based on the western varieties of the low Franconian area
but low Saxon and Frisian elements are also found in this standard
language. Scholars disagree about the precise position of Dutch and Low
German in the language tree. They can be traced back to a common root
often referred to as the Ingwaeonic language group, but are often grouped
together with High German as a separate West Germanic group. This
grouping with High German might be the best representation of the modern
language situation given that the individual dialects spoken in the area in
fact form a dialect continuum. Afrikaans, finally, is a contemporary West
Germanic language, developed from seventeenth century Dutch as a result
of colonization, but with influences from African languages.

The Position of Frisian in the Germanic Language Area 65
2.2. The relationship between Frisian and the other Germanic languages.
This short outline of the relationships among the Germanic languages
shows that English is the language which is genetically closest to Frisian,
and still today English is considered to be most similar to Frisian. For
example The Columbia Encyclopedia (2001) says: “Of all foreign
languages, [Frisian] is most like English”. Pei (1966, p. 34) summarizes the
situation as follows: “Frisian, a variant of Dutch spoken along the Dutch
and German North Sea coast, is the foreign speech that comes closest to
modern English, as shown by the rhyme: ‘Good butter and good cheese is
good English and good Fries’”. This rhyme refers to the fact that the words
for butter and cheese are almost the same in the two languages. However,
in the course of history, contact with other Germanic languages has caused
Frisian to converge to these languages. The Frisians have a long history of
trade and in early medieval times they were one of the leading trading
nations in Europe due to their strategic geographic position close to major
trade routes along the rivers and the North Sea. Also, the Vikings and the
English were frequent visitors of the Frisian language area. This intensive
contact with both English and the North Germanic languages, especially
Danish, resulted in linguistic exchanges (see Feitsma, 1963; Miedema,
1966; Wadstein, 1933). Later in history, the Frisian language was
especially influenced by the Dutch language (which itself contains many
Frisian elements). For a long period, Frisian was stigmatized as a peasant
language and due to the weak social position of the Frisian language in the
Dutch community it was often suppressed, resulting in a strong Dutch
impact on the Frisian language. Nowadays, Dutch as the language of the
administration still has a large influence on the media and there has been
substantial immigration of Dutch speaking people to Friesland. However,
the provincial government has decided to promote Frisian at all levels in
the society.
When investigating the position of the Frisian language within the
Germanic language group, there are clearly two forces which should be
taken into account. On the one hand, Frisian and English are genetically
closely related and share sound changes which do not occur in the other
Germanic languages. This yields the expectation that the linguistic distance
between these two languages is relatively small. On the other hand, the
close contact with Dutch makes it plausible that the Dutch and the Frisian
languages have converged. Also the distance to Danish might be smaller
than expected from the traditional division of Germanic into a North

66 Charlotte Gooskens and Wilbert Heeringa
Germanic and a West Germanic branch at an early stage because of the
intensive contacts in the past.
3. Data sources
In this section, we will first give a short characterization of the language
varieties and the speakers who were recorded for our investigation. Next,
we will present the nature of the recordings and the transcriptions which
formed the basis for linguistic distance measurements.
3.1. Language varieties
Since our main interest was the Frisian language and its linguistic position
within the Germanic language group we wished to represent this language
as well as possible. For this reason, we included seven Frisian varieties,
spread over the Frisian language area. Furthermore, our material contained
eight Germanic standard languages. First, we will describe the Frisian
varieties and next the standard languages.
As far as the Frisian varieties are concerned, we chose varieties from
different parts of the province, both from the coastal area and from the
inland. The varieties are spoken in different dialect areas according to the
traditional classification (see below) and they represent different stages of
conservatism. The precise choice of the seven varieties was determined by
speaker availability for recordings in our vicinity and at the Fryske
Akademy in Leeuwarden. In Figure 2, the geographical position of the
seven Frisian language varieties in the province of Friesland is shown.
Due to the absence of major geographical barriers, the Frisian language
area is relatively uniform. The major dialectal distinctions are primarily
phonological. Traditionally, three main dialect areas are distinguished (see
e.g. Hof, 1933; Visser, 1997): Klaaifrysk (clay Frisian) in the west,
Wâldfrysk (forest Frisian) in the east and Súdwesthoeksk (southwest
quarter) in the southwest. In our material Klaaifrysk is represented by the
dialects of Oosterbierum and Hijum, Wâldfrysk by Wetsens and
Westergeest, and Súdwesthoeksk by Tjerkgaast. Hindeloopen is in the area
of Súdwesthoeksk. However, this dialect represents a highly conservative
area. The phonological distance between Hindeloopen and the main
dialects is substantial (van der Veen, 2001). Finally, our material contains

The Position of Frisian in the Germanic Language Area 67
the variety spoken in Leeuwarden (see note 1). This is an example of Town
Frisian, which is also spoken in other cities of Friesland. Town Frisian is a
Dutch dialect strongly influenced by Frisian but stripped of the most
characteristic Frisian elements (Goossens, 1977).
Oosterbierum
Hindeloopen
Hijum
Leeuwarden
Tjerkgaast
Wetsens
Westergeest
Figure 2. The geographical position of the seven Frisian language varieties in the
province of Friesland.
In addition to the Frisian dialects, the following eight standard languages
were included: Icelandic, Faroese, Norwegian, Swedish, Danish, English,
Dutch, and German. We had meant to include all standard Germanic
languages in our material. However, due to practical limitations a few
smaller languages were not included.
As for Norwegian, there is no official standard variety. The varieties
spoken around the capital of Oslo in the southeast, however, are often
considered to represent the standard language. We based the present
investigation on prior research on Norwegian dialects (see Heeringa and
Gooskens, 2003; Gooskens and Heeringa, submitted), and we chose the
recording which to Norwegians sounded most standard, namely the
Lillehammer recording 3 . It was our aim to select standard speakers from all
countries, but it is possible that the speech of some speakers contains slight
regional influences. The speakers from Iceland, the Faroe Islands and

68 Charlotte Gooskens and Wilbert Heeringa
Sweden spoke the standard varieties of the capitals. The Danish speaker
came from Jutland, the German speaker from Kiel, the English speaker
from Birmingham and the Dutch speaker had lived at different places in the
Netherlands, including a long period in the West during adolescence.
3.2. Phonetic transcriptions
The speakers all read aloud translations of the same text, namely the fable
‘The North Wind and the Sun’. This text has often been used for phonetic
investigations; see for example The International Phonetic Association
(1949 and 1999) where the same text has been transcribed in a large
number of different languages. A database of Norwegian transcriptions of
the same text has been compiled by J. Almberg (see note 3). As mentioned
in the previous section, we only used the transcription of Lillehammer from
this database. In future, we would like to investigate the relations between
Norwegian and other Germanic varieties, using the greater part of the
transcriptions in this database. Therefore, our new transcriptions should be
as comparable as possible with the existing Norwegian ones. To ensure
this, our point of departure was the Norwegian text. This text consists of 91
words (58 different words) which were used to calculate Levenshtein
distances (see Section 4). The text was translated word for word from
Norwegian into each of the Germanic language varieties. We are aware of
the fact that this may result in less natural speech: sentences were often
syntactically wrong. However, it guarantees that for each of the 58 words a
translation was obtained. The words were not recorded as a word list, but as
sentences. Therefore in the new recordings words appear in a similar
context as in the Norwegian varieties. This ensures that the influence of
assimilation phenomena on the results is as comparable as possible.
Most new recordings were transcribed phonetically by one of the
authors. To ensure consistency with the existing Norwegian transcriptions,
our new transcriptions were corrected by J. Almberg, the transcriber of the
Norwegian recordings. In most cases we incorporated the corrections. The
transcription of the Faroese language was completely done by J. Almberg.
The transcriptions were made in IPA as well as in X-SAMPA (eXtended
Speech Assessment Methods Phonetic Alphabet). This is a machinereadable
phonetic alphabet, which is also readable by people. Basically, it
maps IPA-symbols to the 7 bit printable ASCII/ANSI characters 4 . The

The Position of Frisian in the Germanic Language Area 69
transcriptions were used to calculate the linguistic distances between
varieties (see Section 4).
4. Measuring distances between varieties
In 1995 Kessler introduced the use of the Levenshtein distance as tool for
measuring linguistic distances between language varieties. The Levenshtein
distance is a string edit distance measure and Kessler applied this algorithm
to the comparison of Irish dialects. Later on, this approach was applied by
Nerbonne, Heeringa, Van den Hout, Van der Kooi, Otten, and Van de Vis
(1996) to Dutch dialects. They assumed that distances between all possible
pairs of segments are the same. E.g. the distance between an [�] and an [e]
is the same as the distance between the [�] and [�]. Both Kessler (1995) and
Nerbonne and Heeringa (1997) also experimented with more refined
versions of the Levenshtein algorithm in which gradual segment distances
were used which were found on the basis of the feature systems of
Hoppenbrouwers (1988) and Vieregge et. al. (1984).
In this paper we use an implementation of the Levenshtein distance in
which sound distances are used which are found by comparing
spectrograms. In Section 4.1 we account for the use of spectral distances
and explain how we calculate them. Comparisons are made on the basis of
the audiotape The Sounds of the International Phonetic Alphabet (Wells
and House, 1995). In Section 4.2 we describe the Levenshtein distance and
explain how spectral distances can be used in this algorithm.
4.1. Gradual segment distances
When acquiring language, children learn to pronounce sounds by listening
to the pronunciation of their parents or other people. The acoustic signal
seems to be sufficient to find the articulation which is needed to realize the
sound. Acoustically, speech is just a series of changes in air pressure,
quickly following each other. A spectrogram is a “graph with frequency on
the vertical axis and time on the horizontal axis, with the darkness of the
graph at any point representing the intensity of the sound” (Trask, 1996, p.
328).
In this section we present the use of spectrograms for finding segment
distances. Segment distances can also be found on the basis of phonological

70 Charlotte Gooskens and Wilbert Heeringa
or phonetic feature systems. However, we prefer the use of acoustic
representations since they are based on physical measurements. In Potter,
Kopp and Green’s (1947) Visible Speech, spectrograms are shown for all
common English sounds (see pp. 54-56). Looking at the spectrograms we
already see which sounds are similar and which are not. We assume that
visible (dis)similarity between spectrograms reflects perceptual
(dis)similarity between segments to some extent. In Figure 3 the
spectrograms of some sounds are shown as pronounced by John Wells on
the audiotape The Sounds of the International Phonetic Alphabet (Wells
and House, 1995). The spectrograms are made with the computer program
PRAAT 5 .
Figure 3. Spectrograms of some sounds pronounced by John Wells. Upper the [i]
(left) and the [e] (right) are shown, and lower the [p] (left) and the [s]
(right) are visualized.
4.1.1. Samples
For finding spectrogram distances between all IPA segments we need
samples of one or more speakers for each of them. We found the samples
on the tape The Sounds of the International Phonetic Alphabet on which all

The Position of Frisian in the Germanic Language Area 71
IPA sounds are pronounced by John Wells and Jill House. On the tape the
vowels are pronounced in isolation. The consonants are sometimes
preceded, and always followed by an [a]. We cut out the part preceding the
[a], or the part between the [a]’s. We realize that the pronunciation of
sounds depends on their context. Since we use samples of vowels
pronounced in isolation and samples of consonants selected from a limited
context, our approach is a simplification of reality. However, Stevens
(1998, p. 557) observes that
“by limiting the context, it was possible to specify rather precisely the
articulatory aspects of the utterances and to develop models for estimating
the acoustic patterns from the articulation”.
The burst in a plosive of the IPA inventory is always preceded by a period
of silence (voiceless plosives) or a period of murmur (voiced plosives).
When a voiceless plosive is not preceded by an [a], it is not clear how long
the period of silence which really belongs to the sounds lasts. Therefore we
always cut out each plosive in such a way that the time span from the
beginning to the middle of the burst is equal to 90 ms. Among the plosives
which were preceded by an [a] or which are voiced (so that the real time of
the start-up phase can be found) we found no sounds with a period of
silence or murmur which was clearly shorter than 90 ms.
In voiceless plosives, the burst is followed by an [h]-like sound before
the following vowel starts. A consequence of including this part in the
samples is that bursts often do not match when comparing two voiceless
plosives. However, since aspiration is a characteristic property of voiceless
sounds, we retained aspiration in the samples. In general, when comparing
two voiced plosives, the bursts match. When comparing a voiceless plosive
and a voiced plosive, the bursts do not match.
To keep trills comparable to each other, we always cut three periods,
even when the original samples contained more periods. When there were
more periods, the most regular looking sequence of three periods was cut.
The Levenshtein algorithm also requires a definition of ‘silence’. To get
a sample of ‘silence’ we cut a small silent part on the IPA tape. This
assures that silence has approximately the same background noise as the
other sounds.
To make the samples as comparable as possible, all vowel and extracted
consonant samples are monotonized on the mean pitch of the 28
concatenated vowels. The mean pitch of John Wells was 128 Hertz; the

72 Charlotte Gooskens and Wilbert Heeringa
mean pitch of Jill House was 192 Hertz. In order to monotonize the
samples the pitch contours were changed to flat lines. The volume was not
normalized because volume contains too much segment specific
information. For example it is specific for the [v] that its volume is greater
than that of the [f].
4.1.2. Acoustic representation
In the most common type of spectrogram the linear Hertz frequency scale is
used. The difference between 100 Hz and 200 Hz is the same as the
difference between 1000 Hz and 1100 Hz. However, our perception of
frequency is non-linear. We hear the difference between 100 Hz and 200
Hz as an octave interval, but also the difference between 1000 Hz and 2000
Hz is perceived as an octave. Our ear evaluates frequency differences not
absolutely, but relatively, namely in a logarithmic manner. Therefore, in the
Barkfilter, the Bark-scale is used which is roughly linear below 1000 Hz
and roughly logarithmic above 1000 Hz (Zwicker and Feldtkeller, 1967).
In the commonly used type of spectrogram the power spectral density is
represented per frequency per time. The power spectral density is the power
per unit of frequency as a function of the frequency. In the Barkfilter the
power spectral density is expressed in decibels (dB’s). “The decibel scale is
a way of expressing sound amplitude that is better correlated with
perceived loudness” (Johnson, 1997, p. 53). The decibel scale is a
logarithmic scale. Multiplying the sound pressure ten times corresponds to
an increase of 20 dB. On a decibel scale intensities are expressed relative to
the auditory threshold. The auditory threshold of 0.00002 Pa corresponds
with 0 dB (Rietveld and Van Heuven, 1997, p. 199).
A Barkfilter is created from a sound by band filtering in the frequency
domain with a bank of filters. In PRAAT the lowest band has a central
frequency of 1 Bark per default, and each band has a width of 1 Bark.
There are 24 bands, corresponding to the first 24 critical bands of hearing
as found along the basilar membrane (Zwicker and Fastl, 1990). A critical
band is an area within which two tones influence each other’s perceptibility
(Rietveld and Van Heuven, 1997). Due to the Bark-scale the higher bands
summarize a wider frequency range than the lower bands.
In PRAAT we used the default settings when using the Barkfilter. The
sound signal is probed each 0.005 seconds with an analysis window of
0.015 seconds. Other settings may give different results, but since it was

The Position of Frisian in the Germanic Language Area 73
not a priori obvious which results are optimal, we restricted ourselves to the
default settings. In Figure 4 Barkfilters for some segments are shown.
Figure 4. Barkfilter spectrograms of some sounds pronounced by John Wells.
Upper the [i] (left) and the [e] (right) are shown, and lower the
[p] (left) and the [s] (right) are visualized.
4.1.3. Comparison
In this section, we explain the comparison of segments in order to get
distances between segments that will be used in the Levenshtein distance
measure. In a Barkfilter, the intensities of frequencies are given for a range
of times. A spectrum contains the intensities of frequencies at one time.
The smaller the time step, the more spectra there are in the acoustic
representation. We consistently used the same time step for all samples.
It appears that the duration of the segment samples varies. This may be
explained by variation in speech rate. Duration is also a sound-specific
property. E.g., a plosive is shorter than a vowel. The result is that the
number of spectra per segment may vary, although for each segment the
same time step was used. Since we want to normalize the speech rate and
regard segments as linguistic units, we made sure that two segments get the
same number of spectra when they are compared to each other.

74 Charlotte Gooskens and Wilbert Heeringa
When comparing one segment of m spectra with another segment of n
spectra, each of the m elements is duplicated n times, and each of the n
elements is duplicated m times. So both segments get a length of m × n.
In order to find the distance between two sounds, the Euclidean distance
is calculated between each pair of corresponding spectra, one from each of
the sounds. Assume a spectrum e1 and e2 with n frequencies, then the
Euclidean distance is:
Equation 1. Euclidean distance
The distance between two segments is equal to the sum of the spectrum
distances divided by the number of spectra. In this way we found that the
greatest distance occurs between the [a] and ‘silence’. We regard this
maximum distance as 100%. Other segment distances are divided by this
maximum and multiplied by 100. This yields segment distances expressed
in percentages. Word distances and distances between varieties which are
based on them may also be given in terms of percentages.
In perception, small differences in pronunciation may play a relatively
strong role in comparison with larger differences. Therefore we used
logarithmic segment distances. The effect of using logarithmic distances is
that small distances are weighed relatively more heavily than large
distances. Since the logarithm of 0 is not defined, and the logarithm of 1 is
0, distances are increased by 1 before the logarithm is calculated. To obtain
percentages, we calculate ln(distance + 1) / ln(maximum distance + 1).
4.1.4. Suprasegmentals and diacritics
The sounds on the tape The Sounds of the International Phonetic Alphabet
are pronounced without suprasegmentals and diacritics. However, a
restricted set of suprasegmentals and diacritics can be processed in our
system.
Length marks and syllabification are processed by changing the
transcription beforehand. In the X-SAMPA transcription, extra-short

The Position of Frisian in the Germanic Language Area 75
segments are kept unchanged, sounds with no length indication are
doubled, half long sounds are trebled, and long sounds are quadrupled.
Syllabic sounds are treated as long sounds, so they are quadrupled.
When processing the diacritics voiceless and/or voiced, we assume that a
voiced voiceless segment (e.g. [��]) and a voiceless voiced segment (e.g. [d�])
are intermediate pronunciations of a voiceless segment ([t]) and a voiced
segment ([d]). Therefore we calculate the distance between a segment x and
a voiced segment y as the average of the distance between x and y and the
distance between x and the voiced counterpart of y. Similarly, the distance
between a segment x and a voiceless segment y is calculated as the mean of
the distance between x and y and the distance between x and the voiceless
counterpart of y. For voiced sounds which have no voiceless counterpart
(the sonorants), or for voiceless sounds which have no voiced counterpart
(the glottal stop) the sound itself is used.
The diacritic apical is only processed for the [s] and the [z]. We
calculate the distance between [s�] and e.g. [f] as the average of the distance
between [s] and [f] and [�] and [f]. Similarly, the distance between [z�] and
e.g. [v] is calculated as the mean of [z] and [v] and [�] and [v].
The thought behind the way in which the diacritic nasal is processed is
that a nasal sound is more or less intermediate between its non-nasal
version and the [n]. We calculate the distance between a segment x and a
nasal segment y as the average of the distance between x and y and the
distance between x and [n].
4.2. Levenshtein distance
Using the Levenshtein distance, two dialects are compared by comparing
the pronunciation of a word in the first dialect with the pronunciation of the
same word in the second. It is determined how one pronunciation is
changed into the other by inserting, deleting or substituting sounds.
Weights are assigned to these three operations. In the simplest form of the
algorithm, all operations have the same cost, e.g. 1. Assume afternoon is
pronounced as [����t��n��n] in the dialect of Savannah, Georgia, and as
[�����������] in the dialect of Lancaster, Pennsylvania 6 . Changing one
pronunciation into the other can be done as in table 1 (ignoring
suprasegmentals and diacritics for this moment) 7 :

76 Charlotte Gooskens and Wilbert Heeringa
Table 1. Changing one pronunciation into another using a minimal set of
operations.
æ�ft�n�n delete � 1
æft�n�n insert r 1
æft�rn�n subst. �/u 1
æft�rnun
⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯
3
In fact many sequence operations map [�æ�ft��n��n] to [�æft�r�nu�n]. The
power of the Levenshtein algorithm is that it always finds the cost of the
cheapest mapping.
Comparing pronunciations in this way, the distance between longer
pronunciations will generally be greater than the distance between shorter
pronunciations. The longer the pronunciation, the greater the chance for
differences with respect to the corresponding pronunciation in another
variety. Because this does not accord with the idea that words are linguistic
units, the sum of the operations is divided by the length of the longest
alignment which gives the minimum cost. The longest alignment has the
greatest number of matches. In our example we have the following
alignment:
Table 2. Alignment which gives the minimal cost. The alignment corresponds
with table 1.
æ � f t � n � n
æ f t � r n u n
⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯
1 1 1
The total cost of 3 (1+1+1) is now divided by the length of 9. This gives a
word distance of 0.33 or 33%.
In Section 3.1.3 we explained how distances between segments can be
found using spectrograms. This makes it possible to refine our Levenshtein
algorithm by using the spectrogram distances as operation weights. Now
the cost of insertions, deletions and substitutions is not always equal to 1,
but varies, i.e., it is equal to the spectrogram distance between the segment

The Position of Frisian in the Germanic Language Area 77
and ‘silence’ (insertions and deletions) or between two segments
(substitution).
To reckon with syllabification in words, the Levenshtein algorithm is
adapted so that only a vowel may match with a vowel, a consonant with a
consonant, the [j] or [w] with a vowel (or opposite), the [i] or [u] with a
consonant (or opposite), and a central vowel (in our research only the
schwa) with a sonorant (or opposite). In this way unlikely matches (e.g. a
[p] with a [a]) are prevented.
In our research we used 58 different words. When a word occurred in
the text more than once, the mean over the different pronunciations was
used. So when comparing two dialects we get 58 Levenshtein distances.
Now the dialect distance is equal to the sum of 58 Levenshtein distances
divided by 58. When the word distances are presented in terms of
percentages, the dialect distance will also be presented in terms of
percentages. All distances between the 15 language varieties are arranged
in a 15 × 15 matrix.
5. Results
The results of the Levenshtein distance measurements are analyzed in two
ways. First, on the basis of the distance matrix we applied hierarchical
cluster analysis (see Section 5.1). The goal of clustering is to identify the
main groups. The groups are called clusters. Clusters may consist of
subclusters, and subclusters may in turn consist of subsubclusters, etc. The
result is a hierarchically structured tree in which the dialects are the leaves
(Jain and Dubes, 1988). Several alternatives exist. We used the Unweighted
Pair Group Method using Arithmetic averages (UPGMA), since
dendrograms generated by this method reflected distances which correlated
most strongly with the original Levenshtein distances (r=0.9832), see Sokal
and Rohlf (1962).
Second, we ranked all varieties in order of relationship with the standard
languages, Frisian and Town Frisian (see Section 5.2). When ranking with
relation to Frisian, we looked at the average over all Frisian dialects. Since
the ratings with respect to each of the Frisian varieties individually were
very similar averaging was justified.

78 Charlotte Gooskens and Wilbert Heeringa
5.1. The classification of the Germanic languages
Looking at the clusters of language varieties in Figure 5 we note that our
results reflect the traditional classification of the Germanic languages to a
large extent (see Figure 1). On the highest level there is a division between
English and the other Germanic languages. When we examine the group of
other Germanic languages, we find a clear division between the North
Germanic languages and the West Germanic languages. Within the North
Germanic group, we see a clear division between the Scandinavian
languages (Danish, Norwegian and Swedish) on the one hand and the
Faroese and Icelandic on the other hand. In the genetic tree (see Figure 1),
Norwegian is clustered with Icelandic and Faroese. However, due to the
isolated position of Iceland and the Faroes and intensive language contact
between Norway and the rest of Scandinavia, modern Norwegian has
become very similar to the modern languages of Denmark and Sweden. All
varieties spoken in the Netherlands, including the Frisian varieties, cluster
together, and German clusters more closely to these varieties than English.
Figure 5. Dendrogram showing the clustering of the 14 language varieties in our
study. The scale distance shows average Levenshtein distances in
percentages.
All Frisian dialects form a cluster. This clustering corresponds well with
the traditional classification as sketched in Section 3.1. The dialects of
Hijum and Oosterbierum belong to Klaaifrysk and these dialects form a
cluster. The Wâldfrysk dialects of Westergeest and Wetsens also cluster
together. The Levenshtein distance between the four dialects is small,

The Position of Frisian in the Germanic Language Area 79
ranging from 19.6% between Hijum and Oosterbierum and 23.8% between
Oosterbierum and Westergeest. Also the Súdwesthoeksk dialects,
represented by the Tjerkgaast dialect, are rather close to the Klaaifrysk and
Wâldfrysk dialects (distances between 21.6% and 26.4%). The highly
conservative dialect of Hindeloopen is more deviant from the other dialects
(distances between 29.8% and 32.5%) and this is also the case for the Town
Frisian dialect of Leeuwarden which is more similar to Dutch (20.3%) than
to Frisian (between 32.3% and 35.8%) which confirms the characterization
of Town Frisian by Kloeke (1927) as ‘Dutch in Frisian mouth’.
5.2. The relationship between Frisian and the other Germanic languages
From Table 3 and 4 it is possible to determine the distance between all
Germanic standard languages. We are especially interested in the position
of Frisian within the Germanic language group. For this purpose the mean
distance over the 6 Frisian dialects (excluding the dialect of Leeuwarden
which is considered Dutch) has been added. This makes it possible to treat
Frisian as one language. Examining the column which shows the ranking
with respect to Frisian, we find that Dutch is most similar to Frisian (a
mean distance of 38.7%). Clearly the intensive contact with Dutch during
history has had a great impact on the distance between the two languages.
Moreover, German appears to be closer to Frisian than any other language
outside the Netherlands. Looking at the ranking with respect to Dutch, it
appears that Town Frisian is most similar (Leeuwarden 20.3%), followed
by the Frisian varieties (average of 38.7%). Next, German is most similar,
due to common historical roots and continuous contact (a distance of
53.3%).
As discussed in the introduction, Friesland has a long history of
language contact with the Scandinavian countries, and traces of
Scandinavian influences can be found in the Frisian language. The impact
of this contact is reflected in our results only to a limited extent.
Remarkably, the distances to the mainland Scandinavian languages
(Danish, Norwegian and Swedish) are smaller (between 60.7% and 63.3%)
than to English (65.3%) even though the Frisian language is genetically
closer related to English than to Scandinavian (see Section 2.1).

80 Charlotte Gooskens and Wilbert Heeringa
Table 3. Ranked Levenshtein distances in percentages between each of the five
West Germanic languages and the other language varieties in the
investigation.
Frisian Leeuwarden Dutch English German
Dutch 20.3 Leeuw 20.3 Hindel 63.1 Dutch 53.3
Wetsens 32.3 Hindel 37.5 Wetsens 64.4 Leeuw 54.2
Westerg 32.7 Westerg 37.7 Dutch 64.7 Hindel 56.2
Frisian 34.2 Wetsens 38.3 Swedish 64.9 Westerg 56.9
Oosterb 34.3 Tjerkg 38.5 Leeuw 65.1 Oosterb 57.2
Hindel 34.9 Frisian 38.7 Tjerkg 65.2 Tjerkg 57.3
Leeuw 34.2 Tjerkg 35.3 Hijum 38.9 Frisian 65.3 Frisian 57.3
Dutch 38.7 Hijum 35.8 Oosterb 41.3 Hijum 65.8 Hijum 57.5
German 57.3 German 54.2 German 53.3 Westerg 65.8 Wetsens 58.6
Swedish 60.7 Swedish 59.2 Swedish 60.9 Danish 66.7 Swedish 61.0
Norweg 60.9 Norweg 60.0 Norweg 61.4 Faroese 67.1 Danish 63.5
Danish 63.3 Danish 61.1 Danish 63.4 Oosterb 67.2 Norweg 64.0
English 65.3 English 65.1 English 64.7 German 68.1 Faroese 67.1
Faroese 67.7 Faroese 67.5 Faroese 66.1 Norweg 68.6 English 68.1
Icelandic 70.0 Icelandic 69.6 Icelandic 69.2 Icelandic 69.1 Icelandic 68.5
Table 4. Ranked Levenshtein distances in percentages between each of the five
North Germanic languages and the other language varieties in the
investigation.
Danish Swedish Norwegian Icelandic Faroese
Norweg 43.8 Norweg 43.4 Swedish 43.4 Faroese 54.1 Swedish 53.6
Swedish 47.0 Danish 47.0 Danish 43.8 Swedish 58.7 Icelandic 54.1
Faroese 58.5 Faroese 53.6 Faroese 57.2 Norweg 62.6 Norweg 57.2
Leeuw 61.1 Icelandic 58.7 Westerg 59.6 Danish 62.7 Danish 58.5
Westerg 62.2 Hindel 59.2 Leeuw 60.0 German 68.5 Dutch 66.1
Wetsens 62.3 Leeuw 59.2 Hindel 60.2 Tjerkg 69.1 Hindel 67.0
Icelandic 62.7 Westerg 59.6 Tjerkg 60.6 English 69.1 English 67.1
Hijum 62.9 Tjerkg 60.0 Wetsens 60.7 Dutch 69.2 German 67.1
Frisian 63.3 Frisian 60.7 Frisian 60.9 Leeuw 69.6 Westerg 67.4
Hindel 63.4 Dutch 60.9 Dutch 61.4 Hijum 69.8 Leeuw 67.5
Dutch 63.4 German 61.0 Oosterb 61.9 Frisian 70.0 Tjerkg 67.5
German 63.5 Wetsens 61.1 Hijum 62.6 Wetsens 70.1 Frisian 67.5
Tjerkg 63.8 Oosterb 61.4 Icelandic 62.6 Hindel 70.1 Oosterb 67.7
Oosterb 65.2 Hijum 62.7 German 64.0 Oosterb 70.3 Wetsens 68.1
English 66.7 Icelandic 64.9 English 68.6 Westerg 70.3 Hijum 68.2

The Position of Frisian in the Germanic Language Area 81
So, when looking at the results from a Frisian perspective, the close genetic
relationship with English is not reflected in our results. Of the Germanic
languages in our investigation, only Icelandic and Faroese are less similar
to Frisian than English. However, when looking at the results from an
English perspective, we discover that of all Germanic language varieties in
our material the Frisian dialect of Hindeloopen is most similar to English.
As mentioned before, this dialect is highly conservative and furthermore it
is spoken in a coastal place, which provides for easy contact with England.
Also the Frisian dialect of Wetsens is more similar to English than the
remaining Germanic languages. The other Frisian varieties are found
elsewhere in the middle of the ranking. Among the non-Frisian varieties,
Dutch appears to be most similar to English. However, all Germanic
languages, including Frisian and Dutch, show a large linguistic distance to
English, all distances being above 60%. The development of the English
language has thus clearly taken place independently from the other
Germanic languages, which can be explained by the strong influence from
non-Germanic languages, especially French.
Also Icelandic shows a large distance to all other Germanic languages
(from 54.1% to 70.0%), but in the Icelandic case this is explained by the
conservative nature of this language rather than by language contact
phenomena. Faroese is somewhat less conservative, but still shows rather
large distances to the other languages (between 53.6% and 67.7%). The
distances between the other Nordic languages are smaller (between 43.4%
and 47%), as was expected given that the three Scandinavian languages are
mutually intelligible.
6. Conclusions and discussion
Overall, the classification of the Germanic languages resulting from our
distance measurements supports our predictions. This goes for the
classification of the Frisian dialects and also for the rest of the Germanic
languages. We interpret this as a confirmation of the suitability of our
material showing that it is possible to measure Levenshtein distances on the
basis of whole texts with assimilation phenomena typical of connected
speech and with a rather limited number of words.
The aim of the present investigation was to get an impression of the
position of the Frisian language in the Germanic language area on the basis
of quantitative data. The fact that Frisian is genetically most closely related

82 Charlotte Gooskens and Wilbert Heeringa
to English yields the expectation that these two languages may still be
linguistically similar. However, the distance between English and the
Frisian dialects is large. We can thus conclude that the close genetic
relationship between English and Frisian is not reflected in the linguistic
distances between the modern languages. Geographical and historical
circumstances have caused the two languages to drift apart linguistically.
Frisian has been strongly influenced by Dutch whereas English has been
influenced by other languages, especially French.
It would have been interesting to include these languages in our
material. This would have given an impression of their impact on the
English language. At the same time it would also have given us the
opportunity to test the Levenshtein method on a larger language family than
the Germanic family with its relatively closely related languages. It would
also be interesting to include Old English in our material since this would
give us an impression of how modern Frisian is related to the English
language at a time when it had only recently separated from the common
Anglo-Saxon roots to which also Old Frisian belonged.
For many centuries Frisian has been under the strong influence from
Dutch and the Frisian and Dutch language areas share a long common
history. It therefore does not come as a surprise that Dutch is the Germanic
language most similar to the language varieties spoken in Friesland.
It may be surprising that the linguistic distances between Dutch and the
Frisian dialects are smaller than the distances between the Scandinavian
languages (a mean difference of 6%). Scandinavian languages are known to
be mutually intelligible. This means that when, for example, a Swede and a
Dane meet, they mostly communicate each in their own language. This
kind of communication, which is known as semi-communication (Haugen,
1966), is not typical in the communication between Dutch-speaking and
Frisian-speaking citizens in the Netherlands. The two languages are
considered so different that it is not possible for a Dutch-speaking person to
understand Frisian and consequently the Frisian interlocutor will have to
speak Dutch to a non-Frisian person. Our results raise the question whether
semi-communication would also be possible in a Dutch-Frisian situation. If
this is not the case, we may explain this by linguistic and non-linguistic
differences between the Frisian-Dutch situation and the Scandinavian
situation. The Levenshtein distance processes lexical, phonetic and
morphological differences. All three types are present in our transcription,
since word lists are derived from running texts. Syntactic characteristics are
completely excluded from the analysis. It might be the case that certain

The Position of Frisian in the Germanic Language Area 83
characteristics play a larger role for the Levenshtein distances than
desirable in the case of the Scandinavian languages if we were to use the
method for the explaining mutual intelligibility. For example, it is wellknown
among the speakers of Scandinavian languages that many words
end in an ‘a’ in Swedish while ending in an ‘e’ in Danish. Probably people
use this knowledge in an inter-Scandinavian situation. However, this
difference is included in the Levenshtein distances between Swedish and
Danish. It is possible that Frisian-Dutch differences are less predictable or
less well-known by speakers of the two languages. It is also possible that
the difference in communication in the Netherlands and in Scandinavia
should be sought at the extra-linguistic level. Scandinavian research on
semi-communication has shown that the willingness to understand and the
belief that it is possible to communicate play a large role for mutual
intelligibility between speakers of closely related languages.
Staying with the Scandinavian languages, it should be noted that the
mainland Scandinavian languages are in fact closer to Frisian than English,
even though the Scandinavian languages belong genetically to another
Germanic branch than English and Frisian. This can probably be explained
by intensive contacts between Frisians and Scandinavians for many
centuries. However, the common idea among some speakers of Frisian and
Scandinavian that the two languages are so close that they are almost
mutually intelligible is not confirmed by our results, at least not as far as
the standard Scandinavian languages are concerned. Probably this popular
idea is built on the fact that a few frequent words are identical in Frisian
and Scandinavian. It is possible, however, that this picture would change if
we would include more Danish dialects in our material. For example, it
seems to be relatively easy for fishermen from Friesland to speak to their
colleagues from the west coast of Denmark. Part of the explanation might
also be that fishermen share a common vocabulary of professional terms.
Also the frequent contact and a strong motivation to communicate
successfully are likely to be important factors.
As we mentioned in the introduction, among dialects in the Netherlands
and Flanders, the Frisian varieties are most deviant from Standard Dutch.
However, among the varieties which are recognized as languages in the
Germanic language area, Frisian is most similar to Dutch. The smallest
distance between two languages, apart from Frisian, was found between
Norwegian and Swedish: 43.4%. The distance between Frisian and Dutch is
smaller: 38.7%. The Town Frisian variety of the capital of Friesland
(Leeuwarden) has a distance of only 20.3% to Dutch. Although the

84 Charlotte Gooskens and Wilbert Heeringa
recognition of Frisian as second official language in the Netherlands is right
in our opinion, we found that the current linguistic position of Frisian
provide too little foundation for becoming independent from the
Netherlands, as some Frisians may wish 8 .
Acknowledgements
This research would have been impossible without informants who were
willing to translate the story of ‘the Northwind and the Sun’. We wish to
thank G. Blom (Hindeloopen), J. Spoelstra (Hijum) and W. Visser
(Oosterbierum). All of them are affiliated with the Fryske Akademy in
Leeuwarden. We also thank S. van Dellen (Wetsens), T. de Graaf
(Leeuwarden), F. Postma (Tjerkgaast) and O. Vries (Westergeest), all of
them employees of the University of Groningen. We thank J. Allen
(England), A. Mikaelsdóttir (Iceland), Vigdis Petersen (the Faroes), R.
Kraayenbrink (the Netherlands), K. Sjöberg (Sweden) and R. Schmidt
(Germany). We are also very grateful to Jørn Almberg for making available
the recording of Lillehammer (Norway). The recordings and transcriptions
of the Frisian transcriptions are made by the second author, and those of the
standard languages (except Norway and the Faroes) by the first author. The
transcriptions subsequently were checked by Jørn Almberg who we thank
gratefully for correcting our transcriptions. Furthermore, we wish to
express our gratitude to Peter Kleiweg for his software for creating the map
(Figure 2) and visualizing the dendrogram (Figure 5). Finally we thank
Maartje Schreuder for reading an earlier version of this article and giving
useful comments and Angeliek van Hout for reviewing our English.
Notes
1 Dr. Tjeerd de Graaf, the central figure in this volume, was born in Leeuwarden,
the capital of Friesland. Leeuwarden is one of the places where Town Frisian is
spoken. Tjeerd de Graaf is a native speaker of this dialect, but later on he also
learned (standard) Frisian. The Leeuwarden speaker in the present investigation
was Tjeerd de Graaf (see Section 3.1).
2 Most of this section is based on König and Van der Auwera (1994).
3 The Lillehammer recording can be found at http://www.ling.hf.ntnu.no/nos/
together with 52 recordings of other Norwegian dialects.

The Position of Frisian in the Germanic Language Area 85
4 Since our material included two toneme languages, Swedish and Norwegian,
also the two tonemes I and II were transcribed. For the other varieties primary
stress was noted. Stress and tonemes were, however, not included for
calculation of linguistic distances.
5 The program PRAAT is a free public-domain program developed by Paul
Boersma and David Weenink at the Institute of Phonetic Sciences of the
University of Amsterdam and available at http://www.fon.hum.uva.nl/praat.
6 The data is taken from the Linguistic Atlas of the Middle and South Atlantic
States (LAMSAS) and available via: http://hyde.park.uga.edu/lamsas/.
7 The example should not be interpreted as a historical reconstruction of the way
in which one pronunciation changed into another. From that point of view it
may be more obvious to show how [�����������] changed into [����t��n��n]. We
just show that the distance between two arbitrary pronunciations is found on the
basis of the least costly set of operations mapping one pronunciation into
another.
8 Tjeerd de Graaf has never taken such an extreme position. Possibly speakers of
Town Frisian have a more moderate opinion towards this issue since Town
Frisian is more closely related to standard Dutch, as appeared in Figure 5 and
Table 3.
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Learning Phonotactics with Simple Processors
John Nerbonne and Ivilin Stoianov
Abstract
This paper explores the learning of phonotactics in neural networks.
Experiments are conducted on the complete set of over 5,000 Dutch
monosyllables extracted from CELEX, and the results are shown to be
accurate within 5% error. Extensive comparisons to human phonotactic
learning conclude the paper. We focus on whether phonotactics can be
effectively learned and how the learning which is induced compares to
human behavior.
1. Introduction 1
Phonotactics concerns the organization of the phonemes in words and
syllables. The phonotactic rules constrain how phonemes combine in order
to form larger linguistic units (syllables and words) in that language (Laver,
1994). For example, Cohen, Ebeling & van Holk (1972) describe the
phoneme combinations possible in Dutch, which will be the language in
focus in this study.
Phonotactic rules are implicit in natural languages so that humans
require no explicit instruction about which combinations are allowed and
which are not. An explicit phonotactic grammar can of course be abstracted
from the words in a language, but this is an activity linguists engage in, not
language learners in general. Children normally learn a language's
phonotactics in their early language development and probably update it
only slightly once they have mastered the language.
Most work on language acquisition has arisen in linguistics and
psychology, and that work employs mechanisms that have been developed
for language, typically, discrete, symbol-manipulation systems.
Phonotactics in particular has been modeled with n-gram models, Finite

90 John Nerbonne and Ivilin Stoianov
State Machines, Inductive Logic Programming, etc. (Tjong Kim Sang,
1998; Konstantopoulos, 2003). Such approaches are effective, but a
cognitive scientist may ask whether the same success could be possible
using less custom-made tools. The brain, viewed as a computational
machine, exploits other principles, which have been modeled in the
approach known as Parallel Distributed Processing (PDP), which was
thoroughly described in the seminal work of Rumelhart & McClelland
(1986). Computational models inspired by the brain structure and neural
processing principles are Neural Networks (NNs), also known as
connectionist models.
Learning phonotactic grammars is not an easy problem, especially when
one restricts one's attention to cognitively plausible models. Since
languages are experienced and produced dynamically, we need to focus on
the processing of sequences, which complicates the learning task. The
history of research in connectionist language learning shows both successes
and failures even when one concentrates on simpler structures, such as
phonotactics (Stoianov, Nerbonne & Bouma, 1998; Stoianov & Nerbonne,
2000; Tjong Kim Sang, 1995; Tjong Kim Sang & Nerbonne, 1999; Pacton,
Perruchet, Fayol & Cleeremans, 2001).
This paper will attack phonotactics learning with models that have no
specifically linguistic knowledge encoded a priori. The models naturally do
have a bias, viz., toward extracting local conditioning factors for
phonotactics, but we maintain that this is a natural bias for many sorts of
sequential behavior, not only linguistic processing. A first-order Discrete
Time Recurrent Neural Network (DTRNN) (Carrasco, Forcada & Neco,
1999; Tsoi & Back, 1997) will be used: the Simple Recurrent Network
(SRNs) (Elman, 1988). SRNs have been applied to different language
problems (Elman, 1991; Christiansen & Chater, 1999; Lawrence, Giles &
Fong, 1995), including learning phonotactics (Shillcock, Levy, Lindsey,
Cairns & Chater, 1993; Shillcock, Cairns, Chater & Levy, 1997). With
respect to phonotactics, we have also contributed reports (Stoianov et al.,
1998; Stoianov & Nerbonne, 2000; Stoianov, 1998).
SRNs have been shown capable of representing regular languages
(Omlin & Giles, 1996; Carrasco et al., 1999). Kaplan & Kay (1994)
demonstrated that the apparently context-sensitive rules that are standardly
found in phonological descriptions can in fact be expressed within the more
restrictive formalism of regular relations. We begin thus with a device
which is in principle capable of representing the needed patterns.

Learning Phonotactics with Simple Processors 91
We then simulate the language learning task by training networks to
produce context-dependent predictions. We also show how the continuous
predictions of trained SRNs - likelihoods that a particular token can follow
the current context - can be transformed into more useful discrete
predictions, or, alternatively, string recognitions.
In spite of the above claims about representability, the Back-
Propagation (BP) and Back-Propagation Through Time (BPTT) learning
algorithms used to train SRNs do not always find optimal solutions - SRNs
that produce only correct context-dependent successors or recognize only
strings from the training language. Hence, section 3 focuses on the practical
demonstration that a realistic language learning task may be simulated by
an SRN. We evaluate the network learning from different perspectives -
grammar learning, phonotactics learning, and language recognition. The
last two methods need one language-specific parameter - a threshold - that
distinguishes successors/words allowed in the training language. This
threshold is found with a post-training procedure, but it could also be
sought interactively during training.
Finally, section 4 assesses the networks from linguistic and
psycholinguistic perspectives: a static analysis extracts acquired linguistic
knowledge from network weights, and the network performance is
compared to humans' in a lexical decision task. The network performance,
in particular the distribution of errors as a function of string position, will
be compared to alternative construals of Dutch syllabic structure -
following a suggestion from discussions of psycholinguistic experiments
about English syllables (Kessler & Treiman, 1997).
1.1. Motivations for a Phonotactic Device
This section will review standard arguments that demonstrate the cognitive
and practical importance of phonotactics. English phonotactic rules such as:
‘/s/ may precede, but not follow /t/ syllable-initially’
(ignoring loanwords such as `tsar' and `tse-tse') may be adduced by judging
the well-formedness of sequences of letters/phonemes, taken as words in
the language, e.g. /st�p/ vs. */ts�p/. There may also be cases judged to be of
intermediate acceptability. So, even if all of the following are English
words:

92 John Nerbonne and Ivilin Stoianov
/m����/ `mother', /f����/ `father', /s�st��/ `sister'
None of the following are, however:
*/m���/, */f����/, */tss���/
None of these sound like English words. However, the following
sequences:
/m����/, /fu���/, /s�nt��/
"sound" much more like English, even if they mean nothing and therefore
are not genuine English words. We suspect that, e.g., /s�nt��/ 'santer', could
be used to name a new object or a concept.
This simple example shows that we have a feeling for word structure,
even if no explicit knowledge. Given the huge variety of words, it is more
efficient to put this knowledge into a compact form - a set of phonotactic
rules. These rules would state which phonemic sequences sound correct and
which do not. In this same vein, second language learners experience a
period when they recognize that certain phonemic combinations (words)
belong to the language they learn without knowing the meaning of these
words.
Convincing psycholinguistic evidence that we make use of phonotactics
comes from studying the information sources used in word segmentation
(McQueen, 1998). In a variety of experiments, this author shows that word
boundary locations are likely to be signaled by phonotactics. The author
rules out the possibility that other sources of information, such as prosodic
cues, syllabic structure and lexemes, are sufficient for segmentation.
Similarly, Treiman & Zukowski (1990) had shown earlier that phonotactics
play an important role in the syllabification process. According to
McQueen (1998), phonotactic and metrical cues play complementary roles
in the segmentation process. In accordance with this, some researchers have
elaborated on a model for word segmentation: the Possible Word
Constraints Model (Norris, McQueen, Cutler & Butterfield, 1997), in which
likely word-boundary locations are marked by phonotactics, metrical cues,
etc., and in which they are further fixed by using lexicon-specific
knowledge.

Learning Phonotactics with Simple Processors 93
Exploiting the specific phonotactics of Japanese, Dupoux, Pallier,
Kakehi & Mehler (2001) conducted an experiment with Japanese listeners
who heard stimuli that contained illegal consonant clusters. The listeners
tended to hear an acoustically absent vowel that brought their perception
into line with Japanese phonotactics. The authors were able to rule out
lexical influences as a putative source for the perception of the illusory
vowel, which suggests that speech perception must use phonotactic
information directly.
Further justification for the postulation of a neurobiological device that
encodes phonotactics comes from neurolinguistic and neuroimaging
studies. It is widely accepted that the neuronal structure of Broca’s area (in
the brain's left frontal lobe) is used for language processing, and more
specially that it represents a general sequential device (Stowe, Wijers,
Willemsen, Reuland, Paans & Vaalburg, 1994; Reilly, 2002). A general
sequential processor capable of working at the phonemic level would be a
plausible realization of a neuronal phonotactic device.
Besides cognitive modeling, there are also a number of practical
problems that would benefit from effective phonotactic processing. In
speech recognition, for example, a number of hypotheses that explain the
speech signal are created, from which the impossible sound combinations
have to be filtered out before further processing. This exemplifies a lexical
decision task, in which a model is trained on a language L and then tests
whether a given string belongs to L. In such a task a phonotactic device
would be of use. Another important problem in speech recognition is word
segmentation. Speech is continuous, but we divide it into psychologically
significant units such as words and syllables. As noted above, there are a
number of cues that we can use to distinguish these elements - prosodic
markers, context, but also phonotactics. Similarly to the former problem, an
intuitive strategy here is to split the phonetic/phonemic stream at the points
of violation of phonotactic constraints (see Shillcock et al. (1997) and
Cairns, Shillcock, Chater & Levy (1997) for connectionist modeling).
Similarly, the constraints of the letters forming words in written languages
(graphotactics) are useful in word processing applications, for example,
spell-checking.
There is another, more speculative aspect to investigating phonotactics.
Searching for an explanation of the structure of the natural languages,
Carstairs-McCarthy presented in his recent book (1999) an analogy
between syllable structure and sentence structure. He argues that sentences
and syllables have a similar type of structure. Therefore, if we find a proper

94 John Nerbonne and Ivilin Stoianov
mechanism for learning the syllabic structures, we might apply a similar
mechanism to learning syntax as well. Of course, syntax is much more
complex and more challenging, but if Carstairs-McCarthy is right, the basic
principles of both devices might be the same.
2. Simple Recurrent Networks
This section will briefly present Simple Recurrent Networks (Elman, 1988;
Robinson & Fallside, 1988) and will review earlier studies of sequential,
especially phonotactic learning. Detailed descriptions of the SRN
processing mechanisms and the Back-Propagation Through Time learning
algorithm that is used to train the model are available elsewhere (Stoianov,
2001; Haykin, 1994), and will be reviewed only superficially.
Figure 1. Learning phonotactics with the SRNs. If the training data set contains the
words /n�t#/, /n�ts#/ and /n�t��rk#/ then after the network has processed
a left context /n�/, the reaction to an input token /t/ will be active neurons
corresponding to the symbol '#' and the phonemes /s/, and /�/.
Simple Recurrent Networks (SRNs) were invented to encode simple
artificial grammars, as an extension of the Multilayer Perceptron
(Rumelhart, Hinton & Williams, 1986) with an extra input - a context layer
that holds the hidden layer activations at the previous processing cycle.
After training, Elman (1988) conducted investigations on how context

Learning Phonotactics with Simple Processors 95
evolves in time. The analysis showed graded encoding of the input
sequence: similar items presented to the input were clustered at close, but
different, shifting positions. That is, the network discovered and implicitly
represented in a distributed way the rules of the grammar generating the
training sequences. This is noteworthy, because the rules for context were
not encoded, but rather acquired through experience. The capacity of SRNs
to learn simple artificial languages was further explored in a number of
studies (Cleeremans, Servan-Schreiber & McClelland, 1989; Gasser, 1992).
SRNs have the structure shown in Figure 1. They operate as follows:
Input sequences S I are presented to the input layer, one element S I (t) at a
time. The purpose of the input layer is just to transfer activation to the
hidden layer through a weight matrix. The hidden layer in turn copies its
activations after every step to the context layer, which provides an
additional input to the hidden layer - i.e., information about the past, after a
brief delay. Finally, the hidden layer neurons output their signal through a
second weight matrix to the output layer neurons. The activation of the
latter is interpreted as the product of the network. Since the activation of
the hidden layer depends both on its previous state (the context) and on the
current input, SRNs have the theoretical capacity to be sensitive to the
entire history of the input sequence. However, practical limitations restrict
the time span of the context information to maximally 10-15 steps
(Christiansen & Chater, 1999). The size of the layers does not restrict the
range of temporal sensitivity.
The network operates in two working regimens - supervised training and
network use. In the latter, the network is presented the sequential input data
S I (t) and computes the output N(t) using contextual information. The
training regimen involves the same sort of processing as network use and
also includes a second, training step, which compares the network reaction
N(t) to the desired one S T (t), and which uses the difference to adjust the
network behavior in a way that improves future network performance on
the same data.
The two most popular supervised learning algorithms used to train
SRNs are the standard Back-Propagation algorithm (Rumelhart et al., 1986)
and the Back-Propagation Through Time algorithm (Haykin, 1994). While
the earlier is simpler because it uses information from one previous time
step only (the context activation, the current network activations, and
error), the latter trains the network faster, because it collects errors from all
time steps during which the network processes the current sequence and
therefore it adjusts the weights more precisely. However, the BPTT

96 John Nerbonne and Ivilin Stoianov
learning algorithm is also cognitively less plausible, since the collection of
the time-spanning information requires mechanisms specific for the
symbolic methods. Nevertheless, this compromise allows more extensive
research, and without it the problems discussed below would require much
longer training time when using standard computers for simulations.
Therefore, in the experiments reported here the BPTT learning algorithm
will be used. In brief, it works in the following way: the network reaction to
a given input sequence is compared to the desired target sequence at every
time step and an error is computed. The network activation and error at
each step are kept in a stack. When the whole sequence is processed, the
error is propagated back through space (the layers) and time, and weightupdating
values are computed. Then, the network weights are adjusted with
the values computed in this way.
2.1. Learning Phonotactics with SRNs
Dell, Juliano & Govindjee (1993) showed that words could be described
not only with symbolic approaches, using word structure and content, but
also by a connectionist approach. In this early study of learning word
structure with neural nets (NNs), the authors trained SRNs to predict the
phoneme that follows the current input phoneme, given context
information. The data sets contained 100 - 500 English words. An
important issue in their paper is the analysis and modeling of a number of
speech-error phenomena, which were taken as strong support for parallel
distributed processing (PDP) models, in particular SRNs. Some of these
phenomena were: phonological movement errors (reading list - leading
list), manner errors (department - jepartment), phonotactic regularity
violations (dorm - dlorm), consonant-vowel category confusions and initial
consonant omissions (cluster-initial consonants dropping as when `stop' is
mispronounced [t�p]).
Aiming at segmentation of continuous phonetic input, Shillcock et al.
(1997) and Cairns et al. (1997) trained SRNs with a version of the BPTT
learning algorithm on English phonotactics. They used 2 million
phonological segments derived from a transcribed speech corpus and
encoded with a vector containing nine phonological features. The neural
network was presented a single phoneme at a time and was trained to
produce the previous, the current and the next phonemes. The output
corresponding to the predicted phoneme was matched against the following

Learning Phonotactics with Simple Processors 97
phoneme, measuring cross-entropy; this produced a varying error signal
with occasional peaks corresponding to word boundaries. The SRN
reportedly learned to reproduce the current phoneme and the previous one,
but was poor at predicting the following phoneme. Correspondingly, the
segmentation performance was quite modest, predicting only about onefifth
of the word boundaries correctly, but it was more successful in
predicting syllable boundaries. It was significantly improved by adding
other cues such as prosodic information. This means that phonotactics
might be used alone for syllable detection, but polysyllabic word detection
needs extra cues.
In another connectionist study on phonological regularities, Rodd (1997)
trained SRNs on 602 Turkish words; the networks were trained to predict
the following phonemes. Analyzing the hidden layer representations
developed during the training, the author found that hidden units came to
correspond to graded detectors for natural phonological classes such as
vowels, consonants, voiced stops and front and back vowels. This is further
evidence that NN models can capture important properties of the data they
have been trained on without any prior knowledge, based only on statistical
co-occurrences.
Learning the graphotactics and phonotactics of Dutch monosyllables
with connectionist models was first explored by Tjong Kim Sang (1995)
and Tjong Kim Sang & Nerbonne (1999), who trained SRNs to predict
graphemes/phonemes based on preceding segments. The data was
orthogonally encoded, that is, for each phoneme or grapheme there was
exactly one neuron activated at the input and output layers (see below 3.1).
To test the knowledge learned by the network, Tjong Kim Sang and
Nerbonne tested whether the activation of the neurons corresponding to the
expected symbols are greater than a threshold determined as the lowest
activation for some correct sequence encountered during the training data.
This resulted in almost perfect acceptance of unseen Dutch words
(generalization), but also in negligible discrimination with respect to (illformed)
random strings. The authors concluded that “SRNs are unfit for
processing our data set” (Tjong Kim Sang & Nerbonne, 1999).
These early works on learning phonotactics with SRNs prompted the
work reported here. First, Stoianov et al. (1998) demonstrated that the
SRNs in Tjong Kim Sang and Nerbonne's work were learning phonotactics
rather better than those authors had realized. By analyzing the error as a
function of the acceptance threshold, Stoianov et al. (1998) were able to
demonstrate the existence of thresholds successful at both the acceptance of

98 John Nerbonne and Ivilin Stoianov
well-formed data and the rejection of ill-formed data (see below 3.6.2 for a
description of how we determine such thresholds). The interval of highperforming
thresholds is narrow, which is why earlier work had not
identified it (see Figure 2 on how narrow the window is). More recently,
Stoianov & Nerbonne (2000) have studied the performance of SRNs from a
cognitive perspective, attending to the errors produced by the network and
to what extent it correlates with the performance of humans on related
lexical decision tasks. The current article ties these two strands of work and
presents it systematically.
3. Experiments
The challenge in connectionist modeling is not only developing theoretical
frameworks, but also obtaining the most from the network models during
experimentation. This section focuses on experiments on learning the
phonotactics of Dutch syllables with Simple Recurrent Networks and
discusses a number of related problems. It will be followed by a study on
the network behavior from a linguistic point of view.
3.1. Some implementation decisions
SRNs were presented in section 2. A first implementation decision
concerns how sounds are to be represented. A simple orthogonal strategy is
to choose a vector of n neurons to represent n phonemes, to assign each
phoneme (e.g. /�/) to a neuron (e.g., neuron 5 in a sequence of 45), and then
to activate that one neuron and deactivate all the others whenever the
phoneme is to be represented (so a /�/ is represented by four deactivated
neurons, a single activated one, and then forty more deactivated neurons).
This orthogonal strategy makes no assumptions about phonemes being
naturally grouped into classes on the basis of linguistic features such as
consonant/vowel status, voicing, place of articulation, etc. An alternative
strategy exploits such features by assigning each feature to a neuron and
then representing a phoneme via a translation of its feature description into
a sequence of corresponding neural activations.
In phonotactics learning, the input encoding method might be featurebased
or orthogonal, but the output decoding should be orthogonal in order
to obtain a simple prediction of successors, and to avoid a bias induced

Learning Phonotactics with Simple Processors 99
from the peculiarities of the feature encoding scheme used. The input
encoding chosen was also orthogonal, which also requires the network
discover natural classes of phonemes by itself.
The orthogonal encoding implies that we need as many neurons as we
have phonemes, plus one for the end-of-word '#' symbol. That is, the input
and output layers will have 45 neurons. However, it is usually difficult to
choose the right size of the hidden layer for a particular learning problem.
That size is rather indirectly related to the learning task and encoding
chosen (as a subcomponent of the learning task). A linguistic bias in the
encoding scheme, e.g., feature-based encoding, would simplify the learning
task and decrease the number of the hidden neurons required learning it
(Stoianov, 2001). Intuition tells us that hidden layers that are too small lead
to an overly crude representation of the problem and larger error. Larger
hidden layers, on the other hand, increase the chance that the network
wanders aimlessly because the space of possibilities it needs to traverse is
too large. Therefore, we sought an effective size in a pragmatic fashion.
Starting with a plausible size, we compared its performance to nets with
double and half the number of neurons in the hidden layer. We repeated in
the direction of the better behavior, keeping track of earlier bounds in order
to home in on an appropriate size. In this way we settled on a range of 20-
80 neurons in the hidden layer, and we continued experimentation on
phonotactic learning using only nets of this size.
However, even given the right size of the hidden layer, the training will
not always result in an optimal weight set W* since the network learning is
nondeterministic - each network training process depends on a number of
stochastic variables, e.g., initial network weights and an order of
presentation of examples. Therefore, in order to produce more successful
learning, several SRNs with different initial weights were trained in a pool
(group).
The back-propagation learning algorithm is controlled by two main
parameters - a learning coefficient � and a smoothing parameter �. The first
one controls the speed of the learning and is usually set within the range
(0.1…0.3). It is advisable to choose a smaller value when the hidden layer
is larger. Also, this parameter may vary in time, starting with a larger initial
value that decreases progressively in time (as suggested in Kuan, Hornik &
White (1994) for the learning algorithm to improve its chances at attaining
a global minimum in error). Intuitively, such a schedule helps the network
approximately to locate the region with the global minima and later to
make more precise steps in searching for that minimum (Haykin, 1994;

100 John Nerbonne and Ivilin Stoianov
Reed & Marks, II 1999). The smoothing parameter � will be set to 0.7,
which also allows the network to escape from local minima during the
search walk over the error surface.
The training process also depends on the initial values of the weights.
They are set to random values drawn from a region (-r...+r). It is also
important to find a proper value for r, since large initial weight values will
produce chaotic network behavior, impeding the training. We used r = 0.1.
The SRNs used for this problem are schematically represented in Fig. 1,
where the SRN reaction to an input sequence /n�/ after training on an
exemplary set containing the sequences /n�t#/, /n�ts#/, /n�t��rk#/ is given.
For this particular database, the network has experienced the tokens '#', /s/
and /�/ as possible successors to /n��/ during training and therefore it will
activate them in response to this input sequence.
3.2. Linguistic Data - Dutch syllables
A data base L M of all Dutch monosyllables - 5,580 words - was extracted
from the CELEX (1993) lexical database. CELEX is a difficult data source
because it contains many rare and foreign words among its approximately
350,000 Dutch lexical entries, which additionally complicate the learning
task. Filtering out non-typical words is a formidable task and one which
might introduce experimenter prejudice, and therefore all monosyllables
were used. The monosyllables have a mean length of 4.1(� = 0.94; min = 2;
max = 8) tokens and are built from a set of 44 phonemes plus one extra
symbol representing space (#) used as a filler specifying end-of-word.
The main dataset is split into a training (L 1 ) and a testing (L 2 ) database
in proportion approximately 85% to 15%. The training database will be
used to train a Simple Recurrent Network and the testing one will be used
for evaluating the success of word recognition. Negative data also will be
created for test purposes. The complete database L M will be used for some
parts of evaluation.
In language modeling it is important to explore the frequencies of word
occurrences which naturally bias humans' linguistic performance. If a
model is trained on data in proportion to its empirical frequency, this
focuses the learning on the more frequent words and thus improves the
performance of the model. This also makes feasible a comparison of the
model's performance with that of humans performing various linguistic
tasks, such as a lexical decision task. For these reasons, we used the word

Learning Phonotactics with Simple Processors 101
frequencies given in the CELEX database. Because the frequencies vary
greatly ([0...100,000]), we presented training data items in proportion with
the natural logarithms of their frequencies, in accordance with standard
practice (Plaut, McClelland, Seidenberg & Patterson, 1996). This approach
resulted in frequencies in a range of [1...12].
3.3. Difficulty
One way to characterize the complexity of the training set is to compute the
entropy of the distribution of successors, for every available left context.
The entropy of a language L viewed as a stochastic process measures the
average surprise value associated with each element (Mitchell, 1997). In
our case, the language is a set of words and the elements are phonemes,
hence the appropriate entropy measures the average surprise value for
phonemes c preceded by a context s. Entropy is measured for a given
distribution, which in our case is the set of all possible successors. We
compute entropy Entr(s) for a given context s with (1):
Equation 1. Entropy
where � is the alphabet of segment symbols, and p(c) the probability of a
given context. Then the average entropy for all available contexts s∈L,
weighted with their frequencies, will be the measure of the complexity of
the words. The smaller this measure, the less difficult are the words. The
maximal possible value for one context would be log2(45), that is, 5.49, and
this would only obtain for the unlikely case that each phoneme was equally
likely in that context. The actual average value of the entropy measured for
the Dutch monosyllables, is 2.24, � = 1.32. The minimal value was 0.0, and
the maximal value was 3.96. These values may be interpreted as follows:
The minimal value of 0.0 means that there are left contexts with only one
possible successor (log2(1) = 0). A maximal value of 3.96 means that there
is one context which is as unpredictable as one in which 2 3.96 = 16
successors were equally likely. The mean entropy is 2.24, which is to say
that in average 4.7 phonemes follow a given left context.

102 John Nerbonne and Ivilin Stoianov
3.4. Negative Data
We noted above that negative data is also necessary for evaluation. Since
we are interested in models that discriminate more precisely the strings
from L (the Dutch syllables), the negative data for the following
experiments will be biased toward L.
Three negative testing sets were generated and used: First, a set RM containing strings with syllabic form [C] 0...3 V[C] 0...4 , based on the empirical
observation that the Dutch mono-syllables have up to three onset (word
initial) consonants and up to four coda (word final) consonants. The second
group consists of three sub-sets of R M: {R 1 M , R 2 M , R M
3 + }, with fixed distances of
the random strings to any existing Dutch word at 1, 2, and 3+ phonemes,
respectively (measured by edit distance (Nerbonne, Heeringa & Kleiweg,
1999)). Controlling for the distance to any training word allows us to assess
more precisely the performance of the model. And finally, a third group:
random strings built of concatenations of n-grams picked randomly from
Dutch monosyllables. In particular, two sets - R 2 N and R 3 N - were randomly
developed, based on bigrams and trigrams, correspondingly.
The latter groups are the most "difficult" ones, and especially R 3 N ,
because it consists of strings that are closest to Dutch. They are also useful
for the comparison of SRN methods to n-gram modeling. The
corresponding n-gram models will always wrongly recognize these random
strings as words from the language. Where the connectionist predictor
recognizes them as non-words, it outperforms the corresponding n-gram
models, which are considered as benchmark models for prediction tasks
such as phonotactics learning.
3.5. Training
This section reports on network training. We will add a few more details
about the training procedure, then we will present pilot experiments aimed
at determining the hidden layer size. The later parts will analyze the
network performance.

3.5.1. Procedure
Learning Phonotactics with Simple Processors 103
The networks were trained in a pool on the same problem, and
independently of each other, with the BPTT learning algorithm. The
training of each individual network was organized in epochs, in the course
of which the whole training data set is presented in accordance with the
word frequencies. The total of the logarithm of the frequencies in the
training data base L 1 M is about 11,000, which is also the number of
presentations of sequences per epoch, drawn in a random order. Next, for
each word, the corresponding sequence of phonemes is presented to the
input, one at a time, followed by the end-of-sequence marker `#'. Each time
step is completed by copying the hidden layer activations to the context
layer, which is used in the following step.
The parameters of the learning algorithm were as follows: the learning
coefficient � started at 0.3 and dropped by 30% each epoch, finishing at
0.001; the momentum (smoothing) term � = 0.7. The networks required 30
epochs to complete training. After this point, very little improvement is
noted.
3.5.2. Pilot experiments
Pilot experiments aiming at searching for the most appropriate hidden layer
size were done with 20, 40 and 80 hidden neurons. In order to avoid other
nondeterminism which comes from the random selection of negative data,
during the pilot experiments the network was tested solely on its ability to
distinguish admissible from inadmissible successors. Those experiments
were done with a small pool of three networks, each of them trained for 30
epochs, which resulted in approximately 330,000 word presentations or
1,300,000 segments. The total number of individual word presentations
ranged from 30 to 300, according to the individual word frequencies. The
results of the training are given in Table 1, under the group of columns
"Optimal phonotactics". In the course of the training, the networks typically
started with a sharp error drop to about 13%, which soon turned into a very
slow decrease (see Table 2, left 3 columns).
The training of the three pools with hidden layer size 20, 40 and 80,
resulted in networks with similar performance, with the largest network
performing best. Additional experiments with SRNs with 100 hidden
neurons resulted in larger errors than a network with 80 hidden neurons, so

104 John Nerbonne and Ivilin Stoianov
that we settled experimentally on 80 hidden neurons as the likely optimal
size. It is clear that this procedure is rough, and that one needs to be on
guard against premature concentration on one size model.
Table 1. Results of a pilot study on phonotactics learning by SRNs with 20, 40,
and 80 (rows) hidden neurons. Each network is independently trained on
language LM three times (columns). The performance is measured (left 3
columns) using the error in predicting the next phoneme, and (right 3
columns) using L2 (semi-Euclidean) distance between the empirical
context-dependent predictions and the network predictions for each
context in the tree. Those two methods do not depend on randomly
chosen negative data.
Optimal Phonotactics ||SRN L , T L ||L2
Hidd Layer Size SRN1 SRN2 SRN3 SRN1 SRN2 SRN3
20 10.57% 10.65% 10.57% 0.0643 0.0642 0.0642
40 10.44% 10.51% 10.44% 0.0637 0.0637 0.0637
80 10.00% 9.97% 10.02% 0.0634 0.0634 0.0632
Table 2. A typical shape of the SRN error during training. The error drops sharply
in the beginning and then slowly decreases to convergence.
Epoch 1 2-4 5-10 11-15 16-30
Error (%) 15.0 12.0 10.8 10.7 10.5
3.6. Evaluation
The performance of a neural predictor trained on phonotactics may be
evaluated with different methods, depending on the particular task the
network is applied to. In this section we evaluate the neural networks
performing best during the pilot studies.
3.6.1. Likelihoods
The direct outcome of training the sequential prediction task is learning the
successors' distribution. This will therefore be used as a basic evaluation
method: the empirical context-dependent successor distribution Ps L (C) will
be matched against the network context dependent predictions NPs L (C). For

Learning Phonotactics with Simple Processors 105
this purpose, the output of the network will be normalized and matched
against the distribution in the language data. This procedure resulted in a
mean L2 (semi-Euclidean) distance of 0.063 - 0.064, where the optimal
value would be zero (see Table 1, right 3 columns). 2 These values are close
to optimal but baseline models (completely random networks) also result in
approximately 0.085 L2 distance.
3.6.2. Phonotactic Constraints
To evaluate the network's success in becoming sensitive to phonotactic
constraints, we first need to judge how well it predicts individual
phonemes. For this purpose we seek a threshold above which phonemes are
predicted to be admissible and below which they are predicted to be
inadmissible. This is done empirically - we perform a binary search for an
optimal threshold, i.e. the threshold �� that minimizes the network error
E(�). The classification obtained in this fashion constitutes the network's
predictions about phonotactics.
We now turn to evaluating the network's predictions: the method to
evaluate the network from this point of view compares the contextdependent
network predictions with the corresponding empirical
distributions. For this purpose, the method described by Stoianov (2001)
will be used. The algorithm traverses a trie (Aho, Hopcroft & Ullman,
1983: 163-169), which is a tree representing the vocabulary where initial
segments are the first branches. Words are paths through this data structure.
The algorithm computes the performance at the optimal threshold
determined using the procedure described in the last paragraph, i.e., at the
threshold which determines which phonemes are admissible and which
inadmissible (see also 2.1). This approach compares the actual distribution
with the learned distribution, and we normally use the complete database L M
for training and testing.
Figure 2 shows the error of SRN1 8 0 at different values of the threshold.
The optimal threshold searching procedure resulted in 6.0% erroneous
phoneme prediction at a threshold of 0.0175. This means that if we want to
predict phonemes with this SRN, they would be accepted as allowed
successors if the activation of the correspondent neurons are higher than
0.0175.

106 John Nerbonne and Ivilin Stoianov
3.6.3. Word Recognition
Using an SRN trained on phoneme prediction as a word recognizing device
shifts the focus from phoneme prediction to sequence classification. We
wish to see whether it can classify sequences of phonemes into well-formed
words on the one hand and ill-formed non-words on the other. To do this
we need to translate the phoneme (prediction) values into sequence values.
We do this by taking the sum of the phoneme error values for the sequence
of phonemes in the string, normalized to correct for length effects. But to
translate this sum into a classification, we again need to determine an
acceptability threshold, and we use a variant of the same empirical
optimization described above. The threshold arrived at for this purpose is
slightly lower than the optimal threshold from the previous algorithm. This
means that the network accepts more phonemes, which, however, is
compensated for by the fact that a string is accepted only if all its phonemes
are predicted. In string recognition it is better to increase the phoneme
acceptance rate, because the chance to detect a non-word is larger when
more tokens are tested.
Figure 2. SRN error (in %) as a function of the threshold �� The False Negative
Error increases as the threshold increases because more and more
admissible phonemes are incorrectly rejected. At the same time, the False
Positive Error decreases because fewer unwanted successors are falsely
accepted. The mean of those two errors is the network error, which finds
its minimum 6.0% at threshold �� = 0.0175. Notice that the optimal
threshold is limited to a small range. This illustrates how critical the
exact setting of threshold is for good performance.

Learning Phonotactics with Simple Processors 107
Since the performance measure here is the mean percentage of correctly
recognized monosyllables and correctly rejected random strings, we
incorporate both in seeking the optimal threshold. The negative data is as
described above in 3.4. Concerning the positive data, this approach allows
us to test the generalization capacity of the model, so that the training L 1 M
and testing L 2 M subsets may be used here - the first for training the model
and evaluating it during training, and the second to test the generalization
capacity of the trained network.
Once we determine the optimal sequence-acceptance threshold (0.016),
we obtain 5% error on the positive training dataset L 1 M and the negative
strings from RM , where the error varied 0.5% depending on the random data
set generated.
The model was tested further on the second group of negative data sets.
As expected, strings which are more unlike Dutch resulted in smaller error.
Performance on random strings from R N
3 + is almost perfect. In the opposite
case, the strings close to real words (from R 1 N ) resulted in larger error.
The generalization capabilities of the network were tested on the L 2 M
positive data, unseen during training. The error on this test set was about
6%. An explanation of the increase of the error will be presented later,
when the error will be studied by varying its properties.
Another interesting issue is how SRN performance compares to other
known models, e.g. n-grams. The trained SRN definitely outperformed
bigrams and trigrams, which was shown by testing the trained SRNs on the
non-words from R 2 N and R 3 N sets, yielding 19% and 35% error, respectively.
This means that the SRN correctly rejected four out of five non-word
strings composed of correct bigrams and two out of three non-word strings
made of trigrams. To clarify, note that bigram models would have 100%
error on R 2 N , and trigram models 100% error on R 3 N .
4. Network Analysis
The distributed representations in Neural Networks prevent the analysis of
generalizations in trained models by simple observation, which symbolic
learning methods allow. Smaller NNs may be analyzed to some extent by
examination, but for larger networks this is practically impossible.
It is possible, however, to analyze trained networks to extract abstract
knowledge about their behavior. Elman (1988), for example, trained an
SRN to learn sentences and then analyzed the hidden layer activations of

108 John Nerbonne and Ivilin Stoianov
that SRN in various contexts, from which he showed that the network had
internally developed syntactical categories. Similarly, we trained SRNs on
phonotactics (Stoianov et al., 1998), and then analyzed the network
statically, by viewing the weight vectors of each neuron as pattern
classifiers. We showed that the SRN had induced generalizations about
phonetic categories. We follow that earlier work in order to study network
behavior, and we present the results of this study in the first subsection.
Another approach to the analysis of connectionist models assumes that
they are black boxes and examines the variation of network performance
while varying some properties of the data (Plaut et al., 1996; Stoianov,
Stowe & Nerbonne, 1999). For example, one can vary word frequency,
length, etc., and study the network error. When modeling human cognitive
functions with this approach one can compare the behavior of the cognitive
system and its artificial models. For example, in phonotactic modeling, one
can compare results from psycholinguistic studies of a lexical decision task
with the network reaction. This will be subject of study in the rest of the
section.
4.1. Weight Analysis
The neurons of a neural network act as pattern classifiers. The inputs
selectively activate one or another neuron, depending on the weight
vectors. This means that information about network structure may be
extracted from the weight vectors.
In this section we will present a cluster analysis of the neurons in the
output layer. For that purpose, the mean weight vectors of the output layer
of one of the networks - SRN2 4 0 (from Table 1) - were clustered using a
minimum variance (Ward's) method, and each vector in the resulting
dendrogram was labeled with the phoneme it corresponds to. 3 The resulting
diagram is shown in Figure 3.

Learning Phonotactics with Simple Processors 109
Figure 3. Cluster analysis of the vector of the output neurons, labeled with the
phonemes they correspond to. The weight vectors are split into clusters
which roughly correspond to existing phonetic categories.

110 John Nerbonne and Ivilin Stoianov
We can see that the weight vectors (and correspondingly, the phonemes)
cluster into some well-known major natural classes - vowels (in the bottom)
and consonants (the upper part). The vowels are split into two major
categories: low vowels and semi-low, front vowels (/��, �, a, e/), and high,
back ones. The latter, in turn, are clustered into round+ and round- classes.
Consonants appear to be categorized in a way less congruent with
phonetics. But here, too, some established groups are distinguished. The
first subgroup contains non-coronal consonants (/f, k, m, p, x/) with the
exceptions of /l/ and /n/. Another subgroup contains voiced obstruents (/�,
d, �, ���/). The delimiter '#' is also clustered as a consonant, in a group with
/t/, which is also natural. The upper part of the figure seems to contain
phonemes from different groups, but we can recognize that most of these
phonemes are quite rare in Dutch monosyllables, e.g., /�/, perhaps because
they have been 'loaned' from other languages, e.g. /g/.
4.2. Functional analysis
We may also study NNs by examining their performance as a function of
factors such as word frequency, similarity neighborhood, and word length.
Such an analysis relates computational language modeling to
psycholinguistics, and we submit that it is useful to compare the models'
performance with humans'. In this section we introduce several factors
which have played a role in psycholinguistic theorizing. We then examine
the performance of our model as a function of these factors.
4.2.1. Psycholinguistic Factors
Frequency is one of the most thoroughly investigated characteristics of
words that affect performance. Numerous previous studies have
demonstrated that the ease and the time with which spoken words are
recognized are monotonically related to the experienced frequency of
words in the language environment (Luce, Pisoni & Goldinger, 1990; Plaut
et al., 1996). The general tendencies found are that the more frequent words
are, the faster and the more precise they are recognized.
Our perception of a word is likewise known to depend on its similarity
to other words. The similarity neighborhood of a word is defined as the
collection of words that are phonetically similar to it. Some neighborhoods

Learning Phonotactics with Simple Processors 111
are dense with many phonetically similar words while others are sparse
with few.
The so-called Colthearth-N measure of a word w counts the number of
words that might be produced by replacing a single letter of w with some
other. We modify this concept slightly to make it sensitive to similarity of
sub-syllabic elements, so that we regard words as similar when they share
two of the subsyllabic elements - onset, nucleus and coda. Empty onsets or
codas are counted as the same. The word neighborhood is computed by
counting the number of the similar words. If implemented precisely, the
complexity of the measuring process just explained is high, so we reduce it
by probing for sub-syllables rather than for units of variable size, starting
from a single phoneme. This simplifies and speeds up processing. The
neighborhood size of the corpus we used ranged from 0 to 77 and had mean
value of �= 30; � = 13.
For example, the phonological neighborhood of the Dutch word broeds
/bruts/ is given below. Note that the neighborhood contains only Dutch
words.
/br�ts/, /brots/, /bruj/, /brujt/, /bruk/, /brur/, /brus/, /brut/, /buts/, /kuts/,
/puts/, /tuts/
These represent the pronunciations of Brits `British', broods `bread'
(gen.sg.), broei `brew', broeit `brew' (3rd. sg.), broek `pants', broer
`brother', broes `spray nozzle', broed `brood', boots `boots' (Eng. loan),
koets `coach', poets `clean' and toets `test'. Among the words with very poor
neighborhood are /����/ schwung, /b�rts/ boards, /�����jnt/ joint, and
/sk���rs/ squares, all of which are of foreign origin. Words such as /h�k/
hek, /b�s/ bas, /l�xt/ lacht, and /b�kt/ bakt have large neighborhoods.
It is still controversial how similarity neighborhood influences cognitive
processes (Balota, Paul & Spieler, 1999). Intuitively, it seems likely that
words with larger neighborhoods are easier to access due to many similar
items, but from another perspective these words might be more difficult to
access due to the nearby competitors and longer selection process.
However, in the more specific lexical decision task, the overall activity of
many candidates has been shown to facilitate lexical decisions, so we will
look for the same effect here.
The property word length might affect performance in the lexical
decision task in two different ways. On one hand, longer words provide
more evidence since more phonemes are available to decide whether the

112 John Nerbonne and Ivilin Stoianov
input sequence is a word so that we expect higher precision for longer
words, and lower precision for particularly short words. On the other hand,
network error accumulating in iteration increases the error in phoneme
predictions at later positions, which in turn will increase the overall error
for longer words. For these reasons we expect U-shaped patterns of error as
word length increases. Such a pattern was observed in a study on modeling
grapheme-to-phoneme conversion with SRNs (Stoianov et al., 1999). Static
NNs are less likely (than dynamic models such as SRNs) to produce such
patterns.
So far we have presented three main characteristics of the individual
words, which we expect to affect the performance of the model. However, a
statistical correlation analysis (bivariate Spearman test) showed that they
are not independent, which means that an analysis of the influence of any
single factor should control for the rest. In particular, there is high negative
correlation between word neighborhood and word length (r = -0.476),
smaller positive correlation between neighborhood and frequency (r =
0.223), and very small negative correlation between frequency and word
length (r = -0.107). Because of the large amount of data all these
coefficients are significant at the 0.001 level.
Finally, it will be useful to seek a correlate in the simulation for reaction
time, which psycholinguists are particularly fond of using as a probe to
understanding linguistic structure. Perhaps we can find an SRN correlate to
Reaction Time (RT) for the lexical decision task in network confidence,
i.e., the amount of evidence that the test string is a word from the training
language. The less confident the network, the slower the reaction, which
can be implemented with a lateral inhibition (Haykin, 1994; Plaut et al.,
1996). The network confidence for a given word might be expressed as the
product of the activations of the neurons corresponding to the phonemes of
that word. A similar measure, which we call uncertainty U, is the negative
sum of (output) neuron activation logarithms, normalized with respect to
word length |w| (2). Note that U varies inversely with confidence. Less
certain sequences get higher (positive) scores.

Equation 2.
Learning Phonotactics with Simple Processors 113
To analyze the influence of these parameters, the network scores and Uvalues
were recorded for each monosyllabic word at the optimal threshold
��= 0.016. The data was then submitted to the statistical package SPSS for
analysis of variance using SPSS's General Linear Model (GLM). When
analyzing network score, the analysis revealed main effects of all three
parameters discussed above: word neighborhood size (F = 18.4; p <
0.0001), word frequency (F = 19.2; p < 0.0001), word length (F = 11.5; p <
0.0001). There was also interaction between neighborhood size and the
other parameters: the interaction with word frequency had an F -score 6.6
and the interaction of the neighborhood with word length had an F-score of
4.9, both significant at 0.0001 level. Table 3 summarizes the findings. Error
decreases both as neighborhood size and as frequency increases, and error
dependent on length shows the predicted U-shaped form (Table 3c).
Table 3. Effect of (a) frequency, (b) neighborhood density and (c) length on word
uncertainty U and word error.
a.
Frequency Low Mid High
U 2.30 2.20 2.18
Error (%) 8.6 4.1 1.5
b.
Neighb. size Low Mid High
U 2.62 2.30 2.21
Error (%) 12.7 3.9 0.8
c.
Length Low Mid High
U 2.63 2.20 2.13
Error (%) 5.2 4.4 13.1

114 John Nerbonne and Ivilin Stoianov
Analysis of variance on the U-values revealed similar dependencies. There
were main effects of word neighborhood size (F = 58.2; p < 0.0001), word
frequency (F = 45.9; p < 0.0001), word length (F = 137.5; p < 0.0001), as
well as the earlier observed interactions between neighborhood density and
the other two variables: word length (F = 10.4; p < 0.001) and frequency (F
= 5.235; p < 0.005).
The frequency pattern of error and uncertainty variance was expected,
given the increased evidence to the network for more frequent words. The
displayed length effect showed that the influence of error gained in
recursion is weaker than the effect of stronger evidence for longer words.
Also, the pattern of performance when varying neighborhood density
confirmed the hypothesis of the lexical decision literature that larger
neighborhoods makes it easier for words to be recognized as such.
4.3. Syllabic structure
Phonotactic constraints might hint at how the stream of phonemes is
organized in the language processing system. The popular phoneme,
syllable and word entities may not be the only units that we use for lexical
access and production. There are suggestions that in addition, some subsyllabic
elements are involved in those processes, that is, the syllables
might have not linear structure, but more complex representations (Kessler
& Treiman, 1997). For that purpose, we will analyze how the phoneme
prediction error at a threshold of 0.016 - where the network resulted in best
word recognition - is located within words with respect to the following
sub-syllabic elements - onset, nucleus and coda. The particular hypothesis
that will be tested is whether Dutch monosyllables follow the structure
below that was found in English as well (Kessler & Treiman, 1997).
( Onset - Rhyme (Nucleus - Coda) )
The distribution of phoneme error within words (Table 4a) shows that the
network makes more mistakes at the beginning than at the end of words,
where SRN becomes more confident in its decision. This could be
explained with increasing contextual information that more severely
restricts possible phonemic combinations. A more precise analysis of the
error position in the onset, the nucleus and the coda further reveals other
interesting phenomena (Table 4b).

Learning Phonotactics with Simple Processors 115
Table 4. Distribution of phoneme prediction error at a threshold of 0.016 by (a)
phoneme position within words and (b) phoneme position within subsyllables.
Word and Onset positions start from 2, because the prediction
starts after the first phoneme.
a.
Word Position 2 3 4 5 6 7 8
Error (%) 4.3 1.7 1.4 0.6 0.3 0.3 0.00
b.
Sub-syllabes Onset Nucleus Coda
Relative Position 2 3 1 1 2 3
Error (%)
2.6 0.0 4.5 1.0 1.5 2.0
4
2.6
First, error within the coda increases at the coda's end. We attribute this to
error accumulated toward the end of the words, as was predicted earlier.
The mean entropy in the coda (1.32; � = 0.87) is smaller than the mean
entropy in the onset (1.53; � = 0.78), where we do not observe such effects.
So looser constraints are not the reason for the relatively greater error in the
coda. Next, the error at the transition onset-nucleus is much higher than the
error at the surrounding positions, which means that the break between
onset and rhyme (the conjunction nucleus-coda) is significant. This
distribution is also consistent with the statistical finding that the entropy is
larger in the body (the transition point onset-nucleus) (3.45; � = 0.39), than
in the rhyme (1.94; � = 1.21). All this data support the hypothesis that onset
and rhyme play significant roles in lexical access and that the syllabic
structure confirmed for English by Kessler & Treiman (1997) is valid for
Dutch, too.
5. Conclusions
Phonotactic constraints restrict the way phonemes combine in order to form
words. These constraints are empirical and can be abstracted from the
lexicon - either by extracting rules directly, or via models of that lexicon.
Existing language models are usually based on abstract symbolic methods,
which provide good tools for studying such knowledge. But linguistic
research from a connectionist perspective can provide a fresh perspective
about language because the brain and artificial neural networks share
principles of computations and data representations.

116 John Nerbonne and Ivilin Stoianov
Connectionist language modeling, however, is a challenging task.
Neural networks use distributed processing and continuous computations,
while languages have a discrete, symbolic nature. This means that some
special tools are necessary if one is to model linguistic problems with
connectionist models. The research reported in this paper attempted to
provide answers to two basic questions: first, whether phonotactic learning
is possible at all in connectionist systems, which had been doubted earlier
(Tjong Kim Sang, 1995; Tjong Kim Sang, 1998). In the case of a positive
answer, the second question is how NN performance compares to human
ability. In order to draw this comparison, we needed to extract the
phonotactic knowledge from a network which has learned the sequential
structure. We proposed several ways of doing this.
Section 3 studied the first question. Even if there are theoretical results
demonstrating that NNs have the needed finite-state capacity for
phonotactic processing, there are practical limitations, so that we needed
experimental support to demonstrate the practical capability of SRNs to
learn phonotactics. A key to solving the problems of earlier investigators
was to focus on finding a threshold that optimally discriminated the
continuous neuron activations with respect to phoneme acceptance and
rejection simultaneously. The threshold range at which the network
achieves good discrimination is very small (see Figure 2), which illustrates
how critical the exact setting of the threshold is. We also suggested that this
threshold might be computed interactively, after processing each symbol,
which is cognitively plausible, but we postpone a demonstration of this to
another paper.
The network performance on word recognition - word acceptance rate of
95% and random string rejection rate of 95% at a threshold of 0.016 -
competes with the scores of symbolic techniques such as Inductive Logic
Programming and Hidden Markov Models (Tjong Kim Sang, 1998), both
of which reflect low-level human processing architecture with less fidelity.
Section 4 addressed the second question of how other linguistic
knowledge encoded into the networks can be extracted. Two approaches
were used. Section 4.1 clustered the weights of the network, revealing that
the network has independently become sensitive to established phonetic
categories.
We went on to analyze how various factors which have been shown to
play a role in human performance find their counterparts in the network's
performance. Psycholinguistics has shown, for example, the ease and the
time with which spoken words are recognized are monotonically related to

Learning Phonotactics with Simple Processors 117
the frequency of words in language experience (Luce et al., 1990). The
model likewise reflected the importance of neighborhood density in
facilitating word recognition, which we speculated stems from the
supportive evidence which more similar patterns lend to the words in their
neighborhood. Whenever network and human subjects exhibit a similar
sensitivity to well-established parameters, we see a confirmation of the
plausibility of the architecture chosen.
Finally, the distribution of the errors within the words showed another
linguistically interesting result. In particular, the network tended to err
more often at the transition onset-nucleus - which is also typical for
transitions between adjacent words in the speech stream and used for
speech segmentation. Analogically, we can conclude from this that the
nucleus-coda unit - the rhyme - is a significant linguistic unit for the Dutch
language, a result suggested earlier for English (Kessler & Treiman, 1997).
We wind up this conclusion with one disclaimer and a repetition of the
central claim. We have not claimed that SRNs are the only (connectionist)
model capable of dynamic processing, nor that they are biologically the
most plausible neural network. Our central claim is to have demonstrated
that relatively simple connectionist mechanisms have the capacity to model
and learn phonotactic structure.
Notes
1
The authors are particularly pleased to offer this piece to a Festschrift honoring
Dr. Dr. h.c. Tjeerd de Graaf, who graciously agreed to cooperate in the
supervision of Stoianov's Ph.D. project 1997-2001 at the University of
Groningen. Even if Tjeerd is best known for his more recent work on
descriptive linguistics, minority languages and language documentation, his
early training in physics and earlier research on acoustic phonetics made him
one of the best-suited supervisors for projects such as the one reported on here
involving advanced learning algorithms. Tjeerd's sympathy with Eastern
European languages and cultures is visceral and might have led him to agree in
any case, but we particularly appreciated his phonetic acumen.
2
The distance is related to Euclidean, but more exactly the distance between the
two n-dimensional vectors is

118 John Nerbonne and Ivilin Stoianov
3 The cluster analysis in Figure 3 was produced by programs written by Peter
Kleiweg, available at http://www.let.rug.nl/alfa.
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Weak Interactions
Yiddish influence in Hungarian, Esperanto and
Modern Hebrew
Tamás Bíró
When I arrived in Groningen, I was introduced to Tjeerd de Graaf as
somebody speaking Hungarian. Then it turned out that both of us were
interested in Yiddish. Furthermore, we shared the fact that we started our
scientific life within physics, although, unlike Tjeerd, I have not worked as
a physicist since my graduation. Nevertheless, as a second year physics
student I received a research question from the late leading Hungarian
physicist George Marx that was also somehow related to Tjeerd’s earlier
research topic, neutrino astrophysics.
Neutrinos are funny particles. They are extremely light, if they have any
mass, at all. 1 Therefore, they cannot interact through gravitation. Because
they do not have any electrical charge either, electromagnetic interaction is
also unknown to them. The only way they can interact with the universe is
the so-called weak interaction, one of the four fundamental forces. 2
Nowadays physicists spend an inconceivable amount of budget building
gigantic, underground basins containing millions of liters of heavy water
just to try to detect a few neutrinos per year out of the very intense stream
of neutrinos flowing constantly from the Sun and going through the Earth,
that is, us. Even though they almost never interact with regular material,
through weak interaction they play a fundamental role both in shaping what
the universe looks like and in the Sun’s energy production. Therefore our
life would not be possible without neutrinos and without weak interaction.
Something similar happens in ethnolinguistics. The interaction between
two languages may not always be very salient, and it cannot necessarily be
explained by the most famous types of interactions. A weak interaction in
linguistics might be an interaction which is not acknowledged by the
speakers’ community, for instance for ideologically reasons.
In the present paper I shall present three cases of weak interaction
between languages, understood in this sense, namely Yiddish affecting

124 Tamás Bíró
Hungarian, Modern Hebrew (Israeli Hebrew) and Esperanto. All the stories
take place in the late nineteenth or early twentieth century, when a new or
modernized language had to be created. We shall observe what kind of
interactions took place under which conditions. A model for interactions
combined with the better understanding of the social-historical setting will
enable us to do so.
1. Language interactions within a given socio-historical setting
1.1. Modeling interactions
In physics, the interaction between two bodies depends on three factors: the
two “eligibilities” of the parties to interact, as well as their distance. For
gravity and electromagnetism, the formula probably familiar from highschool
physics states that the force is proportional to the product of the
“eligibilities” - mass or electric charge - of the two bodies, divided by the
square of their distance. In other words, the higher the two masses (or
electric charges) and the smaller the distance, the stronger the interaction.
For Newton, who formulated this formula first, gravity was a long-range
interaction. Modern physics has completed this picture with introducing
exchange particles intermediating between the interacting bodies. 3 That
way, contemporary science has also incorporated the view of Newton’s
opponents who argued for the only possibility of short-range interactions.
To transplant this image, vaguely, into the phenomenon of language
interaction, we have to identify the eligibilities of the two interacting
languages, their distance and the exchange particles. In fact, we can do that
even on two levels. On a purely linguistic level, one can easily point to
words and grammatical phenomena - “exchange particles” - wandering
from language to language. But it would be harder to identify in general the
properties of the phenomena and of the given languages that make the
interaction more probable or less probable.
The sociolinguistic level is more promising for such an approach. In this
case, the human beings are the exchange particles: people who leave one
linguistic community in order to join a new one. By the very fact of their
moves, they affect their new language by a linguistic quantum. The closer
the two language communities, the more people will act as an exchange

Weak Interactions 125
particle. Here distance should be understood not only based on geography,
but on the intensity of the social network, as well. Thus, the more people
wander to the target community, the more linguistic impulse is brought to
the second language and therefore the stronger the interaction. Note that the
physical analogy is not complete, since the symmetry of action and reaction
is not guaranteed for interacting languages.
The three cases to be discussed share the feature that the role of the
carriers of the interaction is played by late nineteenth century Eastern
European Jews. In order to understand the historical background, we have
to recall what is called Haskala or Jewish Enlightenment.
1.2. The Haskala
By the late eighteenth century, the French and German Aufklärung had
raised the question whether to emancipate and integrate - or assimilate - the
Jewish population on the one side, and an increasing wish to join the
European culture on the other. Although in the second half of the siècle des
lumières there were only a few Jewish intellectuals who articulated these
ideas, most of them belonging to the circle of the philosopher Moses
Mendelssohn (1729-1786) in Berlin, the next decades witnessed the
acculturation of a growing segment of the Jewish population in the German
territories, as well as within the Austrian Empire. The eighteenth century
Berlin Haskala is called the first stage of the Jewish Enlightenment,
whereas the early nineteenth century social and cultural developments
represent its second stage.
What the first two stages of the Haskala yielded was including a Jewish
color on the contemporary Western European cultural palette. “Jewish” was
understood exclusively as one possible faith within the list of European
religions, and nothing more than a religious conviction. An enlightened Jew
was supposed to fully master the educated standard variant of the language
of the society he lived in (Hochdeutsch¸ in most of the cases), without any
“Jewish-like” feature. Propagating the knowledge of Hochdeutsch and
rolling back Jüdischdeutsch had already been the program of Moses
Mendelssohn when he began writing a modern targum 4 of the Bible, the
Biur. Further, the same Jew was expected to fully master the contemporary
European culture, including classical languages, sciences and arts. The only
sphere in which this Jew could express his or her being Jewish was the
diminished and europeanized arena of religious life. Diminished, because

126 Tamás Bíró
of a secularization of life style; and Europeanized, due to the inclusion of
philosophical ideals of the Enlightenment together with aesthetic models of
the Romanticism. The traditional religious duty of constantly learning the
traditional texts with the traditional methods was sublimated into the
scholarly movement of the Wissenschaft des Judentums.
The picture changed dramatically in the middle of the nineteenth
century, when the Haskala, in its third stage, reached the Eastern European
Jewry, including Jews in Poland and Lithuania (under Russian
government), Eastern Hungary, and Rumania. Here the Jewish population
was far denser, whereas the surrounding society was far behind Western
Europe in the process of the social and economic development. In fact,
Jews would play an important role in the modernization of those areas.
Therefore, several people of Jewish origin could take the initiative and
invent absolutely new alternatives to the social constructs that people had
been living with so far.
One type of those social alternatives still preserved the idea of the
earlier Haskala according to which Jews should become and remain an
organic part of the universal human culture. These alternatives proposed
thus some forms of revolutionary change to the entire humankind, as was
the case in the different types of socialist movements, in which Jews
unquestionably played an important role. Esperantism also belongs here,
for its father, Ludwig Zamenhof was a Polish-Lithuanian Jew proposing an
alternative to national language as another social construct.
The second type of radical answer that Eastern European Jews gave to
the emergence of Enlightenment in the underdeveloped Eastern European
milieu was creating a new kind of Jewish society. Recall that there was a
dense Jewish population living within a society that itself did not represent
a modern model to which most Jews wished to acculturate. Different
streams of this type of answer emerged, although they did not mutually
exclude each other. Many varieties of political activism, such as early
forms of Zionism, political Zionism, territorialism or cultural autonomism,
embody one level of creating an autonomous Jewish society.
The birth of a new Jewish secular culture, including literature,
newspapers or Klezmer music is another one. The question then arose
whether the language of this new secular culture should be Yiddish - and
thus a standardized, literary version of Yiddish was to be developed - or
Hebrew - and therefore a renewal of the Hebrew language was required. In
the beginning, this point was not such an enormous matter of dispute as it
would later develop into, when “Hebraists”, principally connected with

Weak Interactions 127
Zionism, confronted “Yiddishists”, generally claiming a cultural and / or
political autonomy within Eastern Europe. It is the irony of history that the
far more naïve and seemingly unrealistic ideology, calling for the revival of
an almost unspoken language in the distant Palestine, was the one that later
would become reality.
1.3. Language interactions in the Haskala
Let us now return to our model of language interactions. As we have seen,
the intensity of the interaction depends on the number of “exchange
particles” - language changing individuals - , that is a kind of “distance”
measured in the social network; furthermore on the “eligibility” of the
languages to transmit and to adopt features. We shall now confront this
model with the linguistic reality of the different stages of the Haskala.
Concerning the first stage, when only a handful of followers of Moses
Mendelssohn rejected the Jüdischdeutsch and started speaking
Hochdeutsch, our model will correctly predict that the number of exchange
particles is insufficient to affect German in a perceptible way.
The number of exchange particles increases dramatically when we reach
the first half of the nineteenth century. However, the people changing
language more or less consciously adopted the idea of their original idiom
being an unclean and corrupt version of the target language. Consequently,
by nature their language change consisted of not bringing any influence on
the target language with them. By applying our vague physical model to
this situation, we might say that although the two languages were indeed
close - from the viewpoints of geography, linguistic similarity and social
contacts - , Hochdeutsch was not “eligible” enough to be seriously affected.
What happened in the third stage of the Haskala? The following three
case studies represent three possibilities. The first one, the influence of
Yiddish on Hungarian, was actually a case where some elements of stage 2
Haskala were still present. The emancipation of the Jews was closely
related to their assimilation into the Hungarian society, culture and
language. As Jews wished to become an equal part of that society, let us
call this case type e. Each of the many people brings only a very “light”
quantum of influence, similarly to the very little mass, if any, of the
electron neutrinos. The type mu designates a case when Jews migrated to a
newly created Jewish “land, language and culture”, namely to Modern
Hebrew. Here less people carry possibly more “weight”, that is why they

128 Tamás Bíró
can be paralleled by the heavier muon neutrinos. In the third case, that is
the birth of Esperanto, only one person of Jewish cultural background
wished to transform the entire word, with a total rejection of reference to
any form of Jewishness, at least on a conscious level (type tau, referring to
the probably heaviest type of neutrinos).
2. Three examples of weak interaction
2.1. Type e: Yiddish and Hungarian
Nineteenth century Hungary was situated on the border of Western
European Jewry, affected already by the first two stages of Haskala, and
Eastern European Jewry, which would be reached only by its third phase.
From the second half of the previous century onward, the Jewish
immigration from Bohemia and Moravia had been importing a rather
urbanized population speaking Western Yiddish, or even Jüdischdeutsch,
whereas Eastern Yiddish speaking Galician Jews inhabiting Eastern
Hungary represented the westernmost branch of Eastern European Jewry.
Not only were the linguistic features of the two groups strikingly different,
but also their social, economic and cultural background.
In the social and economic fields, Hungary met a first wave of
modernization in the 1830s and 1840s, which is referred to as the reform
age, reaching its peak in the 1848-49 revolution. After the so-called
Compromise with Austria in 1867, the consequence of which had been the
creation of the Austro-Hungarian Empire with a dualistic system, the most
urbanized parts of the country showed an especially remarkable economic
and cultural growth.
Parallel to the phenomenon of general modernization, the Jewish
population underwent a similar process to the one we have already seen
apropos of the French and German Jewry that had gone through these
social changes fifty years earlier. The second quarter of the century already
witnesses a few Jewish thinkers, mainly rabbis arriving from Germany or
Bohemia, and bringing modern ideals with them. Yet, their effect cannot be
perceived on a larger social scale before the last third of the century.
A few differences should, however, be noted between German and
Hungarian Haskala. First, for the larger society into which Hungarian Jews

Weak Interactions 129
wished to integrate, Enlightenment was not so much the consequence of the
Embourgeoisement, rather its catalyst. Enormous heterogeneities in the
degree of development could be found within the country, both in social, as
well as economic terms. This general picture was paralleled with a
heterogeneous distribution of Eastern and Western type of Jewry. Thus,
even if the most Europeanized Jews may have wished, they could not
disown their pre-Haskala coreligionists living close to them.
Moreover, the modern Hungarian society and culture had to be created
in spite of the Austrian occupation. Social constructs underwent huge
changes, and any group of people identifying themselves as Hungarian -
and not Austrian - could influence the new shapes of society and culture.
Immigrants from all directions played a fundamental role in laying down
the bases of modern Hungarian urban culture. These are the circumstances
under which most of the Jews chose the Hungarian, rather than the German
or Yiddish culture and language. This decision was far from being evident.
Even most of the orthodoxy adopted Hungarian, though more slowly and
by keeping simultaneously Yiddish.
By putting together the pieces, we obtain an image in which the
dynamically changing Hungarian culture and society is searching new,
modern forms, and is ready to integrate foreign influences - as long as the
carriers identify themselves as new Hungarians. Further, a major part of the
Jewish population is seeking its place in this new society, wants to adopt
the new culture, but is still strongly connected - often against its will - to
the pre-Haskala Jewry living not so far from them. Consequently, we have
both a high “eligibility” for being influenced on the part of the Hungarian
language, and a large number of “exchange particles” flowing from Yiddish
to Hungarian. 5
What is the outcome of such a situation? Let us consider a few examples
of Yiddishisms in Hungarian. I shall distinguish between three registers that
Yiddishisms entered considerably: the Jewish sociolect of Hungarian, argot
(slang), and standard Hungarian.
The vocabulary of Hungarian speaking Jews unsurprisingly includes a
large number of words specific to domains of Jewish culture and religion.
In some cases only phonological assimilation takes place. The
Hungarian phonological system lacks a short /a/, and the short counterpart
of /��� is /�/. Therefore the Yiddish word [��������] (‘Rosh Ha-shana, name
of the Jewish New Year’, from Hebrew [��� ������], i.e. [���� �������� in
standard Hungarian Ashkenazi pronunciation) becomes optionally
[��������]. Although the original Yiddish pronunciation [��������] is still

130 Tamás Bíró
possible, the latter emphasizes the foreign origin of the word. An analogous
example is the word barchesz ([������] or [������], ‘chala, a special bread
used on Shabbat and holidays’), which is clearly from Yiddish origin, but is
unknown outside Hungary; it may have belonged to the vocabulary of
Hungarian Yiddish.
Other words immediately underwent Hungarian morphological
processes. In fact, it is a well known phenomenon in many languages of the
world that borrowed verbs, unlike borrowed nouns, cannot be integrated
directly into the vocabulary of a given language. This is the case in words
like lejnol (‘to read the Torah-scroll in the synagogue’), lejnolás (‘the
reading of the Torah-scroll’) as well as snóder (‘money given as donation’),
snóderol (‘to donate money, especially after the public Torah-reading’),
snóderolás (‘the act of money donation’). In the first case, the Yiddish verb
leyenen (‘idem’) 6 was borrowed and one of the two most frequent
denominal verbal suffixes, -l, was added. 7 The word lejnolás is the nomen
actionis formed with the suffix -ás. The expression tfilint légol (‘to put on
the phylacteries’) originates from German and Yiddish legen, and has gone
through the same processes. For snóderol, Hungarian borrows a Yiddish
noun, 8 which then serves as the base of further derivations.
The Jewish sociolect of Hungarian includes further lexical items, which
do not belong to the domain of religious practice or Jewish culture. One
such word is unberufn (‘without calling [the devil]’), which should be
added out of superstition to any positive statement that the speaker hopes to
remain true in the future. For instance: ‘My child grows in beauty,
unberufn’ (Blau-Láng, 1995:66). Nowadays, many people of the generation
born after World War II and raised already in an almost non-Yiddish
speaking milieu judge this expression as having nothing to do with
superstition, but qualifying a situation as surprisingly good, like ‘You don’t
say so! It’s incredible!’ and definitely including also some irony. 9 Others of
that generation say in the same surprising-ironic context: “My grandma
would have said: unberufn…”, even if Grandma had used that word in a
slightly different way. This second meaning of unberufn clearly lacks any
reference to superstition, since the same people would use another
expression (lekopogom) to say ‘touch wood! knock on wood!’.
Unlike the previous interjections, the adjective betámt (‘nice, intelligent,
smart, sweet, lovely’) already enters the “real” syntax of the target
language, even if morphological and phonological changes have not taken
place yet - which happened in the case of lejnol and snóderol. The word
betámt consists of the Hebrew root taam (‘taste’), together with the

Weak Interactions 131
Germanic verbal prefix be- and past participle ending –t. The resulting
word denotes a person who “has some taste”: somebody who has some
characteristic traits, who is interesting, who has style and some sense of
humour, which is kind, polite, and so on. It is typically used by “Yiddishe
mammes” describing the groom they wish their daughter had.
So far, we have seen examples where the language changing population
has kept its original expression to denote something that could be best
expressed using items of their old vocabulary. This Jewish sociolect has
become an organic part of modern Hungarian, acknowledged, and partially
known by many non-Jewish speakers, as well. But do we also find
influences of Yiddish outside of the Jewish sociolect?
The register that is the most likely to be affected under such
circumstances is probably always slang: it is non-conformist by definition,
and, therefore, it is the least conservative. Slang is also the field where
social norms, barriers and older prejudices play the least role. This may be
the reason why Hungarian slang created in the nineteenth century borrowed
so much from the languages of two socially marginal groups: the Gipsy
(Roma) languages and Yiddish. In contemporary Hungarian slang, one can
find well-known words from Yiddish origin such as: kóser (‘kosher’,
meaning ‘good’ in slang); tré (‘bad, crappy, grotty’, from Hebrew-Yiddish-
Hungarian tréfli ‘ritually unclean, non kosher food’); majré (‘fear, dread,
rabbit fever’, from Hebrew mora ‘fear’ > Ashkenazi [�����] > Yiddish
moyre [�����] > Hungarian [������]), further derived to majrézik (‘to fear,
to be afraid of sg.’); szajré (‘swag, loot, hot stuff’, from Hebrew sehora
������� �������������� ��� �� �� ������ et al., 1967-76). An interesting
construction is stikában, meaning ‘in the sly, in secret, quitely’. Its origin is
the Aramaic-Hebrew noun ������] ‘remaining silent’, which receives a
Hungarian inessive case ending, meaning ‘in’.
Through slang, some of the Yiddish words have then infiltrated into the
standard language and become quasi-standard. Thus, the word haver - from
the Hebrew ������] ‘friend’ - is used nowadays as an informal synonym for
a ‘good acquaintance, a friend’. Similarly, dafke means in spoken
Hungarian ‘For all that! Only out of spite!’. Furthermore, there are words
of Yiddish origin which did not enter Hungarian through the slang, but
through cultural interaction: macesz (‘matzo, unleavened bread’, from
Hebrew matzot, plural form of matza; its ending clearly shows that the
word arrived to Hungarian through Yiddish) or sólet (‘tsholent’, a typically
Hungarian Jewish bean dish, popular among non-Jews, too). 10

132 Tamás Bíró
To summarize, the high amount of “exchange particles”, that is, Jewish
people gradually changing their language from Yiddish to Hungarian, has
affected the target language in three manners. One of them has been the
creation of a special Jewish sociolect. This was not a secret language
though, and non-Jews have borrowed quite a few expressions. This fact led
to the second manner of influence, namely to the high amount of Yiddish
words entering the slang. Some of these words have infiltrated even into the
relatively more informal registers of the standard language. The third
manner is cultural interaction: the exchange of cultural goods - for instance
in the field of gastronomy - inevitably has resulted the exchange of the
vocabulary designating those goods.
2.2. Type µ: Yiddish and Modern Hebrew
The fruit of Western European Haskala in the field of science was the birth
of Wissenschaft des Judentums. The Jewish scholars belonging to this
group aimed to introduce modern approaches when dealing with traditional
texts, Jewish history, and so forth. Their approach contrasted traditional
rabbinical activity the same way as the romanticist cantorial compositions
by Salomon Sulzer and Louis Lewandowski contrasted traditional synagogal
music: modernists aimed to produce cultural goods that were esteemed by
the modern society, both by Jews and the recipient country. A further
motivation of the Wissenschaft des Judentums was to expose the values of
post-Biblical Jewish culture, and to present them as an organic part of
universal culture: by emancipating Jewish past, they hoped to be also
emancipated by contemporary society.
This background illuminates why early Haskala honored so much
Hebrew - the language of the contribution par excellence of the Jewish
nation to universal culture, which is the Hebrew Bible, and a language that
had been long studied by Christian Hebraists. And also why Yiddish, the
supposedly jargon of the uneducated Jews and a corrupt version of German,
was so much scorned in the same time.
Although the goal of the earlier phases of Haskala was to promote the
literary language of the recipient country among Jews, that is practically
Hochdeutsch, and Hebrew was principally only the object of scholarly
study, still some attempts were made to use the language in modern
domains, at least for some restricted purposes. After a few pioneering
experiments to establish Hebrew newspapers in the middle of the

Weak Interactions 133
eighteenth century, the Hebrew literary quarterly Ha-Meassef appeared as
early as 1784 (Sáenz-Badillos, 1993:267).
However, it was not before the middle of the next century, when
Haskala reached Russia, that the need of reviving the Hebrew language was
really articulated. As already discussed, the major reasons for this switch
were that the Jewish population did not see the underdeveloped
surrounding society as a model to which they wanted to assimilate; the
Russian society and policy did not show any real sign of wanting to
emancipate and integrate Jews, either; furthermore, the huge Jewish
population reached the critical mass required to develop something in itself.
The summation of these factors led to the idea of seeing Jewry as separate a
nation in its modern sense. A further factor reinforcing Jewish national
feelings both in Eastern and Western Europe was the emergence of modern
political anti-Semitism in the 1870s in the West, accompanied by events
such as the huge Russian pogroms in 1881, the blood libel of Tiszaeszlár,
Hungary (1882-3) or the Dreyfus-affair in France (starting in 1894).
The claims following from this idea were that the Jewish nation has the
right to have a country - in Palestine or elsewhere, but at least it should
receive some local autonomy - , and also that the Jewish nation must have
its own national language. The two major candidates for the Jewish
national language were Yiddish and Hebrew, although German was not out
of the competition, either (cf. e.g. Shur, 1979:VII-VIII).
The first wave of attempts to revive Hebrew consisted mainly of purists,
seeing Biblical Hebrew as the most precious layer of the language: some of
them went so far that they preferred to create very complicated expressions
to designate modern concepts, rather than using non-Biblical vocabulary.
The fruits of this early period are among others the first regular Hebrew
weekly, Ha-Maggid (1856), the first modern play by D. Zamoscz (1851),
novels by A. Mapu, as well as works of S. J. Abramowitsch (Mendele
Moykher Seforim), who can be considered one of the founders of both
modern Hebrew and modern Yiddish literature.
The real upswing was observable in the last quarter of the century,
especially after the 1881 pogroms, and when Haskala had reached the
broadest masses, as well. Traditionally, the publication of Eliezer Ben-
Yehuda’s article in 1879 entitled ‘A burning question’ is considered to be
the opening of the new era (Sáenz-Badillos, 1993:269). Ben-Yehuda (1858-
1922) has been portrayed as the hero of the revival: he moved to Jerusalem
in 1881, where he forced himself and his family to speak Hebrew. To speak
a language, that is to produce everyday, spontaneous sentences “in real-

134 Tamás Bíró
time”, on a language that had been mostly used for writing and reading and
only in restricted domains. His son, Ithamar (1882-1943), was the first
person after millennia who grew up in an exclusively Hebrew-speaking
environment. Ben-Yehuda constantly introduced new words designating
weekday concepts, while he was editing a newspaper and working on his
monumental Thesaurus, which incorporated material from ancient and
medieval literature. In 1890, he founded the Va’ad ha-Lashon (‘Language
Committee’), the forerunner of the Hebrew Language Academy, hereby
creating a quasi-official institution for language planning.
However, Shur (1979) has argued against an overestimation of Ben-
Yehuda’s role. Out of Fishman’s five stages of language planning (in Shur,
1979) (1. code selection; 2. ideologization of the choice; 3. codification; 4.
elaboration and modernization; 5. standardization, i.e. the acceptance by the
community), Ben-Yehuda was salient especially in codification and
elaboration, as well as in vitalization, which was also necessary under the
given circumstances. But for socio-political reasons, he had not much
influence on the initial language choice and its ideologization, as well as on
the final acceptance of the codified and elaborated standard.
It is clear that Yiddish was the mother tongue, or one of the main
languages for a major fraction of the members of the Va’ad ha-Lashon,
including Ben-Yehuda himself. Moreover, people with Yiddish as first
language represented an important part of the speaker community of the
old-new tongue in the first half of the twentieth century. Yiddish was not
scorned anymore, as it had been a century before, but it was not considered
as a major source for language reform, either. Especially for the later
generations, Yiddish would symbolize the Diaspora left behind by the
Zionist movement.
Yiddish speaking “exchange particles” dominated the community, much
more than in the Hungarian case. Yet, a very conscious ideology required
changing the previous ethnic language to the old-new national language,
especially after the 1913-14 “Language Quarrel”, wherein the defenders of
Hebrew defeated those of German and Yiddish (Shur, 1979:VII-VIII, X).
This ideology was actively present in almost each and every individual who
had chosen to move to the Land of Israel in a given period - contrary to the
European case, where ideology of changing the language was explicit only
in the cultural elite. Further, the language change was not slow and gradual,
but drastic in the life of the people emigrating to Palestine, combined with a
simultaneous radical change in geographical location, social structure and
lifestyle. What phenomena would this constellation involve?

Weak Interactions 135
Yiddish influence on Modern Hebrew vocabulary has been investigated
by - among others - Haim Blanc. For instance, the Modern Hebrew
interjection davka (approx. ’necessarily, for all that’) is clearly a
Hebraisation of Yiddish dafke, of Hebrew origin itself, and mentioned also
in relation with Hungarian. Similarly, kumzitz ‘get-together, picnic,
campfire’ undoubtedly originates from the Yiddish expression ‘come [and]
sit down!’, since only in Yiddish do we find [u] in the verb ‘to come’.
However, the expression was probably coined in Hebrew, as standard
Yiddish dictionaries do not mention it. One can easily imagine the early
pioneers sitting around a campfire in the first kibbutzim, chatting in a
mixture of Yiddish and Hebrew, and inviting their comrades to join them.
Nissan Netzer (1988) analyses the use of the Modern Hebrew verb
firgen and the corresponding de-verbal noun firgun. Officially, the word is
still not considered to belong to the language, for it is not attested in any
dictionary of Hebrew that I know. Definitions for this word I have found on
the Internet are: “the ability to allow someone else to enjoy if his or her
enjoyment does not hurt one,” and “to treat favorably, with equanimity, to
bear no grudge or jealousy against somebody,” and also “to be delighted at
the success of the other”. The word can be traced back to Yiddish farginen
‘not begrudge, not envy, indulge’. As Netzer has demonstrated, there is a
linguistic gap in Hebrew, for the expressions darash et tovato shel… or lo
hayta eno tsara be- that should bear that meaning are cumbersome,
circuitous, overly sophisticated in style and seems to cloud the true
linguistic message. Therefore, they were not accepted by the linguistic
community. When a leading Hebrew linguistics professor used the Yiddish
equivalent in the early sixties, the situation made the listeners of an
academic lecture smile, because in that time the Yiddishism was considered
to be a folk idiom that would finally withdraw in favor of a “real Hebrew
expression”. However, firgen would have become more and more accepted
in daily conversation and even in journalistic writings by the eighties. 11
This example has led us to the issue of the sociolinguistic status of
Yiddish words in Modern Hebrew. Ora Schwarzwald (1995) shows that the
vocabulary of the most used classical texts, such as the Hebrew Bible and
liturgy, has become the base of Modern Hebrew, in all its registers.
Furthermore, loanwords of European languages are also used both in
formal and non-formal language. However, from less esteemed languages,
such as Jewish languages (e.g. Yiddish and Ladino), as well as Arabic,
words would infiltrate primarily into lower registers and everyday informal
speech.

136 Tamás Bíró
For instance, chevre ‘friends’ is used mainly when addressing
informally a group of people, and it is the borrowing of the similar word in
Yiddish (khevre ‘gang, bunch of friends, society’). The latter obviously
comes from Hebrew chevra ‘society, company, gathering’, whose root is
chaver ‘friend’, a well-known word for speakers of Hungarian and Dutch
(gabber), too. The originally Hebrew word thus arrived back to Modern
Hebrew, but keeping the phonological traces of its trajectory. Also note the
minor shifts in the semantics during the two borrowings.
Another example for Yiddish influence on informal speech is the use of
the -le diminutive suffix: abale from aba ‘dad’, Sarale ‘little Sarah’,
Chanale ‘little Hanah’, and so forth. Observe that the suffix follows the
Hebrew word, whereas in Yiddish one would have Sorele and Chanele
expect.
Thus, the influence of Yiddish on Modern Hebrew is indeed similar to
its influence on Hungarian: lower registers and informal speech constitute
one of the canals through which this interaction takes place. To make the
similarity even more prominent, we can point to two further canals, shared
by the Modern Hebrew case and the Hungarian case. Similarly to
Hungarian, the designation of goods of general culture, such as food names
(beygelach ‘bagels or pretzel’) represent a domain for word borrowings.
Moreover, Yiddish loan words, or Hebrew words with a Yiddish or
Ashkenazi pronunciation are likely to appear in religious vocabulary (e.g.
rebe ‘Chasidic charismatic leader’); typically in the sociolect of religious
groups (especially within the ultra-orthodox society), and in the language
used by secular Israelis to mock the stereotypically Yiddish-speaking ultraorthodox
Jews (e.g. dos ‘an ultra-orthodox person’, from Hebrew dat
‘religion’; vus-vus-im ‘the Ashkenazi ultra-orthodox Jews’, who often say
Vus? Vus? ‘What? What?’ followed by the Hebrew plural ending -im).
2.3. Type τ: Yiddish and Esperanto
Esperanto emerged in the very same context as Modern Hebrew. Its creator,
Lazar Ludwik Zamenhof (1859-1917), was born one year after Eliezer Ben-
Yehuda, similarly from a Jewish family living in a small Lithuanian town,
whose population was composed of Russian, Polish and Lithuanian people,
but was dominated by a Jewish majority. The Litvak (Lithuanian-Jewish)
Haskala background of both men encouraged traditional Jewish education
combined with studies in a secular Gymnasium; both of them went on to

Weak Interactions 137
study medicine. Following the 1881 wave of pogroms, in the year in which
Ben-Yehuda moved to Jerusalem, Zamenhof published an article calling for
mass emigration to a Jewish homeland. For a few years, he became one of
the first activists of the early Zionist movement Hovevei Tzion (“Lovers of
Zion”). Berdichevsky (1986) points out the similarities even in the
mentality and the physical appearance of Zamenhof and Ben-Yehuda.
Nevertheless, two key differences should be pointed out. The first one is
Zamenhof’s pragmatism. In his 1881 article, Zamenhof imagined the
Jewish homeland to be in the western part of the United States, a relatively
unsettled area those days, which would have arisen much less sensibility
from all sides. Furthermore, Zamenhof shared the skepticism of many of
his contemporaries in the feasibility to revive the Hebrew language.
According to the anecdote, Theodor Herzl said once that he could not buy
even a train ticket in Hebrew. Leading Jewish writers, such as Mendele
Moykher Seforim, oscillated between writing in Yiddish and in Hebrew;
both of these languages called for the establishment of a modern, secular
literary tongue. The young and pragmatic Zamenhof chose to reform
Yiddish, the language with millions of native speakers; whereas the first
native speaker of Modern Hebrew, the son of Ben-Yehuda was not born
yet.
In his early years, Zamenhof wrote a comprehensive Yiddish grammar
(completed in 1879, partially published in 1909 in the Vilna Journal, Lebn
un Vissenschaft, and fully published only in 1982). He argued for the
modernization of the language and fought for the use of the Latin alphabet,
instead of the Hebrew one. How is it possible then that a few years later
Zamenhof changed his mind, and switched to Esperanto (1887)?
Here comes the second key difference into the picture. Ben-Yehuda was
sent by his orthodox family to a yeshiva (traditional school teaching mainly
the Talmud), where one of the rabbis introduced him secretly into the
revolutionary ideas of the Haskala. On the contrary, Zamenhof’s father and
grandfather were enlightened high-school teachers of Western languages
(French and German). For him, being Jewish probably meant a universal
mission to make the world a better place for the whole humankind. This
idea originates from eighteenth century German Haskala philosophers
claiming that Judaism is the purest embodiment so far existing of the
universal moral and of the faith of the Pure Reason; even today a major part
of Jews worldwide perceive Judaism this way.
Zamenhof did not therefore content himself with the goal of creating a
Jewish national language. For him, similarly to his semi-secularized

138 Tamás Bíró
coreligionists joining the socialist movement in the same decades, unifying
the human race and building a new word order presented the solution for -
among others - the problems of the oppressed Eastern European Jewry.
And also the other way around: the secular messianic idea of the unification
of the dispersed and oppressed Jews into a Jewish nation was just one step
behind from the secular messianic idea of the unification of the whole
mankind into a supra-national unit. This explains not only the motivations
of Zamenhof himself, but also why Jews played such an important role in
the pre-World War II Esperanto movement in Central and Eastern Europe
(Berdichevsky, 1986:60). Whereas socialists fought for a social-economic
liberation of the oppressed, Zamenhof spoke about the liberation of the
humans from the cultural and linguistic barriers. It is not a coincidence that
the twentieth century history of the Esperantist movement was so much
intermingled with the one of the socialist movements.
Zamenhof’s initiative was to create a language that would be equally
distant from and equally close to each ethnic language, thus each human
being would have equal chance using this bridge connecting cultures and
people. Hence Zamenhof created a vocabulary and a grammar using
elements of languages he knew: Russian (the language his father spoke
home and the language of his highschool), German and French (the
languages his father and grandfather were teachers of), Polish (the language
of his non-Jewish fellow children), Latin and Greek (from highschool), as
well as English and Italian. Note that the resulting language, similarly to
most artificial languages, is inherently European and Indo-European in its
character, though extremely simplified.
However, one should not forget that Zamenhof’s native tongue was
Yiddish, this was the language he used with his school mates in the Jewish
primary school (kheyder, cf. Piron, 1984), and most of his life he kept
contact with circles where Yiddish was alive. So one would wonder why
Yiddish is not mentioned overtly among the source languages of Esperanto.
Seeing Zamenhof’s former devotion for the Jewish sake and the Yiddish
language, as well as his later remark that Yiddish is a language similar to
any other (in Homo Sum (1901), cf. Piron (1984:17) and Berdichevsky
(1986:70)), the possibility that he despised “the corrupt version of German”
or that he felt shame at his Yiddish origins, are out of question.
The challenging task now is to find at least covert influences of Yiddish
on Esperanto.
As strange as it may sound, a considerable literature has been devoted to
etymology within Esperanto linguistics. One of the biggest mysteries is the

Weak Interactions 139
morpheme edz. As a root, it means ‘married person’ (edzo ‘husband’;
edzino ‘wife’, by adding the feminine suffix -in-). While as a suffix, it turns
the word’s meaning into the wife or husband of the stem: lavistino ’washerwoman’
vs. lavistinedzo ‘washerwoman’s housband’; doktoro ‘doctor’ vs.
doktoredzino ‘doctor’s wife’. Hungarian Esperantists have tried to use this
suffix to translate the Hungarian suffix -né (‘wife of…’, e.g.: Deákné ‘wife
of Deák, Mrs. Deák’; cf. Goldin (1982:28)). The phonemic content of the
morpheme is not similar to any word with related meaning in any of the
languages that Zamenhof might have taken into consideration.
Zamenhof himself wrote in a letter to Émile Boirac that the morpheme
was the result of backformation, and that originally it was a bound form
(Goldin, 1982:22f). Boirac suggested in 1913 the following reconstruction:
if the German Kronprinz (‘heir apparent’) became kronprinco in Esperanto,
while Kronprinzessin (‘wife of a crown prince’, note the double feminine
ending: the French feminine suffix -esse is followed by the Germanic
feminine -in) turns to kronprincedzino, then the ending -edzin- can be
identified as ‘a woman legally bound to a man’. By removing the feminine
suffix -in-, we obtain the morpheme -edz-. Goldin adds to this theory that
the morphemes es and ec had already been used with other meanings, that
is why the surprising [��] combination appeared. Summarizing, the
etymology of the Esperanto morpheme edz would be the French feminine
ending -esse, which had been reanalyzed with a different meaning due to
the additional feminine suffix in German.
However, this is not the end of the story. Other alternatives have been also
proposed. Waringhien and others (in Goldin, 1982) have brought forward
the idea that the word serving as the base of backformation was the Yiddish
word rebetsin (‘wife of a rabbi’). In fact, this word can be reanalyzed as
reb+edz+in, and we obtain the edz morpheme using the same logic as
above. Goldin’s counterargument that the Yiddish word is actually rebetsn
with a syllabic [���] is not at all convincing: old Yiddish spelling often uses
the letter yod to designate a schwa, or even more the syllabicity of [�� an �],
similarly to the in German spelling, like in wissen. Consequently, I can
indeed accept the idea that a pre-YIVO spelling rebetsin was in the mind of
Zamenhof.
Piron (1984) adds further cases of possible Yiddish influence. In words
taken from German, the affricate [���] always changes to [�]: German pfeifen
‘to whistle’ became Esperanto fajfi. This coincides with Yiddish fayfn.
Though, one is not compelled to point to Yiddish as the origin of this word:
the reason can simply be that the affricate [���] is too typical to German, not

140 Tamás Bíró
occurring in any other languages that served “officially” as examples for
Zamenhof. In other words, [���] was not seen as universal enough. But what
about the consonant clusters ���], ���], ���], which are also characteristic
solely to German (and to Yiddish)? May the solution be that while [���]
becomes [�] in Yiddish, these clusters are unchanged; therefore, Zamenhof
felt less discomfort with regard to the latter clusters than with regard to [���]
which truly occurs exclusively in German? I do not believe that we can do
more than speculate about the different unconscious factors acting within a
person more than a hundred years ago. The only claim we can make is that
some of these factors must have been related to Yiddish, as expected from
the fact that Yiddish was one of the major tongues of Zamenhof.
In the field of semantics, Piron brings the differentiation in Esperanto
between landa (‘national, related to a given country’, adjective formed
from lando ‘country’) as opposed to nacia (‘national, related to a given
nation’, adjective from nacio ‘nation’). This differentiation exists in
Yiddish (landish and natsional), but not in any other languages that
Zamenhof might have taken into consideration. Piron also argues against
the possible claim that this is not a Yiddish influence, rather an inner
development related to the inner logic of Esperanto.
The most evident example of Piron is Esperanto superjaro ‘leap year’, a
compound of super ‘on’ and jaro ‘year’. No known language uses the
preposition on or above to express this concept. However, Yiddish has
iberyor for ‘leap year’, from Hebrew ibbur (‘making pregnant’), the term
used in rabbinic literature for intercalating an extra month and making the
year a leap year (e.g. Tosefta Sanhedrin 2:1-7). On the other hand, iber also
means ‘above’ in Yiddish, which explains the strange expression in
Esperanto. I do not know if Zamenhof realized that the Yiddish expression
iberyor is not related to German über, but this is probably not relevant.
Let us summarize this section. Yiddish influence on Esperanto is a case
where there is only one exchange particle - in the first order approximation,
at least, since we have not dealt with the possible influences related to the
numerous later speakers of Esperanto of Yiddish background. Though, this
one particle had a huge impact on the language for a very obvious reason.
Even if he did not overtly acknowledge that Yiddish had played a role in
creating Esperanto, it is possible to discover the - either consciously hidden
or unconscious - traces of Yiddish.
Did Zamenhof want to deny that he had also used Yiddish, as a building
block of Esperanto? Perhaps because his goal was indeed to create a
universal, supra-national language, and not the language of the Jewish

Weak Interactions 141
nation? Or, alternatively, was this influence unconscious? I do not dare to
give an answer.
3. Conclusion
In linguistics, we could define weak interaction as an interaction that is not
overtly acknowledged. No one would deny the influence of the Frenchspeaking
ruling class on medieval English, or the impact of the Slavic
neighbors on Hungarian. But sometimes, conscious factors hide the effect.
Yet, weak interactions are as crucial for the development of a language, as
the nuclear processes emitting neutrinos in the core of the Sun that produce
the energy which is vital for us.
We have seen three cases of weak interaction between languages. In
fact, all three stories were about the formative phase of a new or
modernized language, in the midst of the late nineteenth century Eastern
Europe Jewry. In the cases of Yiddish influencing Hungarian and Modern
Hebrew, the number of “exchange particles”, that is, the amount of initially
Yiddish-speaking people joining the new language community, were
extremely high: roughly one tenth of the Hungarian speaking population in
nineteenth century Hungary, and probably above 50% of the Jews living in
early twentieth century Palestine. Nonetheless, in both cases we encounter
an ideology promoting the new language and disfavoring Yiddish.
Because the level of consciousness of this ideology seems to be
inversely proportional to the ratio of “exchange particles” - stronger in
Palestine than in Hungary - , the two factors extinguish each other, and we
find similar phenomena. For instance, Yiddish has affected first and
foremost lower registers, which are less censored by society; therefrom it
infiltrates into informal standard language. Additional trends are Yiddish
words entering specific domains, such as gastronomy or Jewish religious
practice. Although it is essential to note that not all concepts that are new in
the target culture are expressed by their original Yiddish word: many new
expressions in these domains have been coined in Hungarian and Modern
Hebrew, and accepted by the language community.
The third case that we have examined is different. Zamenhof was a
single person, but as the creator of Esperanto, he had an enormous
influence on the new language. The influence of Yiddish was again weak in
the sense that it was not overtly admitted; however, we could present
examples where the native tongue of Zamenhof influenced the new

142 Tamás Bíró
language. We could have cited, as the articles mentioned had done,
numerous further instances where the influence of Yiddish cannot be
proven directly, the given phenomenon could have been taken from other
languages, as well; however, one can hypothesize that Yiddish played -
consciously or unconsciously - a reinforcing role in Zamenhof’s decisions.
I do hope that I have been able to prove to the reader that seemingly
very remote fields, such as physics, social history and linguistics, can be
interconnected, at least for the sake of a thought experiment. Furthermore,
“exchange particles” in the field of science, and Tjeerd is certainly among
them, have hopefully brought at least some weak interaction among the
different disciplines.
Notes
1 According to http://cupp.oulu.fi/neutrino/nd-mass.html, the mass of the
electron neutrino (νe) is less than 2.2 eV, the mass of the muon neutrino (νµ)
does not exceed 170 keV, while the mass of the tau neutrino (νπ) is reported to
be bellow 15.5 MeV. For the sake of comparison, the mass of an electron is 511
keV, while the mass of a proton is almost 940 MeV.
2 Physical phenomena are thought to be reducible to four fundamental forces.
These are gravity, electromagnetism, weak interaction and strong interaction.
The last two play a role in sub-atomic physics.
3 The photons (particles of the light) are the exchange particles for the
electromagnetic interaction; the hypothetical gravitons should transmit gravitation;
in the case of the weak interaction, the W + , W - and Z vector bosons play
that role; whereas the strong interaction is mediated by pions.
4 Targumim (plural of targum) are the Jewish Aramaic versions of the Hebrew
Bible from the late antiquity, including also many commentaries beside the
pure translation. The same way as late antiquity Jews created the commented
translation of the Holy Scriptures to their native tongue and using their way of
thinking, Moses Mendelssohn expected his version of the Bible to fit the
modern way of thinking and the “correct language” of its future readers.
Obviously, the Biur should first have to fulfil its previous task, namely to teach
the modern way of thinking and the “correct tongue” to the first generation of
its readers. Interestingly enough, script was not such a major issue for Mendelssohn
as “language purity”, thus he wrote Hochdeutsch in Hebrew characters; in
order to better disseminate his work among the Jewish population.
5 I assume that the formative phase of modern Dutch society and culture in the
17 th and 18 th century is comparable to that of 19 th century Hungary; even more

Weak Interactions 143
is so the role of Jewry in both countries, as a group which was simultaneously
integrating into the new society and also forming it. In both cases, the presence
of the continuous spectrum from the pre-Haskala Yid to the self-modernizing
Israelite led to a gradual, though determined giving up of the Yiddish language.
This socio-historical parallelism could partially explain why phenomena of
Yiddish influence on Dutch are often similar to that on Hungarian.
Concerning Dutch-Jewish linguistic interactions, readers interested in Jewish
aspects of Papiamentu, a creole language spoken in the Netherlands Antilles,
are referred to Richard E. Wood’s article in Jewish Language Review 3
(1983):15-18.
6 The etymology of the Yiddish word itself is also interesting. The origin is the
late Latin or Old French root [���] ‘to read’ (cf. to Latin lego, legere, modern
French je lis, lire), which was borrowed by the Jews living in early medieval
Western Europe. The latter would then change their language to Old High
German, the ancestor of Yiddish. At some point, the meaning of the Old French
word was restricted to the public reading of the Torah-scroll in the synagogue.
7 Compare to sí ‘ski’ > síel ‘to ski’, ��� ‘fire’ > tüzel ‘to fire’; also: printel ‘to
print with a computer printer’. It is extremely surprising that the word lejnol
does not follow vowel harmony, one would expect * lejnel. Even though the [�]
sound can be transparent for vowel harmony, this fact is not enough to explain
the word lejnol. Probably the dialectal Yiddish laynen was originally borrowed,
and this form served as the base for word formation, before the official Yiddish
form leynen influenced the Hungarian word. Some people still say lájnol.
8 When being called to the Torah during the public reading, one recites a
blessing, the text of which says: “He Who blessed our forefathers Abraham,
Isaac and Jacob, may He bless [the name of the person] because he has come up
to the Torah / who has promised to contribute to charity on behalf of… etc.”
The part of the text ‘who has promised’ sounds in the Ashkenazi pronunciation
[�� �������]. This is most probably the source of the word snóder, after vowel
in the unstressed last syllable has become a schwa, a process that is crucial for
understanding the Yiddishization of Hebrew words. The exciting part of the
story is that the proclitic [��] (‘that’) was kept together with the following finite
verbal form ([�����] ‘he promised’), and they were reanalysed as one word.
9 When I asked people about the meaning of unberufn on the mailing list 2nd-
Generation-Jews-Hungary@yahoogroups.com, somebody reported that her
non-Jewish grandmother also used to say unberufn with a similar meaning.
10 Other Hungarian words of Hebrew origin do not come from Yiddish, as shown
by their non-Askenazi pronunciation: Tóra ([�����] ‘Torah’, as opposed to its
Yiddish counterpart Toyre) or rabbi (and not rov or rebe). Words like behemót
(‘big hulking fellow’), originally from Biblical Hebrew behema (‘cattle’, plural:

144 Tamás Bíró
behemot; appearing also as a proper name both in Jewish and in Christian
mythology) should be rather traced back to Christian Biblical tradition.
11 Note, that the word has kept its original word initial [�], without transforming it
into [�], which would have been predicted by Hebrew phonology. Although
this is a remarkable fact for Netzer, it turns out that almost no word borrowed
by Modern Hebrew would change its initial [�] to [�]. Even not verbs that have
had to undergo morpho-phonological processes (e.g. fibrek from English to
fabricate). The only exception I have found in dictionaries is the colloquial
form pilosofiya for filosofiya ‘philosophy’, as well as the verb formed from it,
pilsef ‘to philosophise’. Furthermore, it can be argued that pilosofiya is not even
a modern borrowing. The only reason why one would still expect firgen
������� ��� ����������� �� ������ ��������� �� ���� ��� ������� �������� �
��
�� ��� ����� ������� �� � ����� ���� �� ��� ��������� ���������à ���
���
�� �������� ������ ����� ��������� ��� ������� ��]. On the other hand, one
�
may claim that /�/ and /�/ should be considered as distinct phonemes in Modern
Hebrew, even if no proposed minimal pair that I know of is really convincing.
References
������ ������ �� ��� ������ ������ ����� ������ A Magyar Nyelv Történeti-
Etimológiai Szótára [The Historical-Etymological Dictionary of
The Hungarian Language]. Akadémiai Kiadó, Budapest.
Berdichevsky, Norman (1986). Zamenhof and Esperanto. Ariel, A Review of
Arts and Letters in Israel, 64: 58-71.
Blau Henrik, Károly Láng (1995). Szájról-szájra, Magyar-jiddis
�������������� ����� ����� ������� ��������� ���� 2 .
Goldin, Bernard (1982). The Supposed Yiddish Origin of the Esperanto
Morpheme. edz, Jewish Language Review, 2: 21-33.
Graaf, Tjeerd, de (1969). Aspects of neutrino astrophysics. Wolters-Noordhoff
nv, Groningen.
Netzer, Nissan (1988). “Fargen” - Employing a Yiddish Root to Bridge a
Linguistic Gap in the Hebrew Language (in Hebrew, with
English abstract). Hebrew Computational Linguistics, 26: 49-58.
Piron, Claude (1984). Contribution à l’étude des apports du yidiche à
l’ésperanto. Jewish Language Review, 4: 15-29.
Sáenz-Badillos, Angel (1993). A History of the Hebrew Language. University
Press, Cambridge.
Schwarzwald, Ora (Rodrigue) (1995). The Components of the Modern Hebrew
Lexicon: The Influence of Hebrew Classical Sources, Jewish

Weak Interactions 145
Languages and Other Foreign Languages on Modern Hebrew (in
Hebrew, with English abstract). Hebrew Linguistics, 39: 79-90.
Shur, Shimon (1979). Language Innovation and Socio-political Setting: The
Case of Modern Hebrew. Hebrew Computational Linguistics, 15:
IV-XIII.

Prosodic Acquisition: a Comparison of Two
Theories
Angela Grimm
1. Introduction
During language development, children’s word productions are target of a
variety of prosodic processes as e.g. syllable deletions, syllable additions
and stress shift. Using current phonological theory, investigators have
explained the production pattern in a number of different ways.
In this paper, I review two approaches to the development of word
stress: Fikkert’s (1994) theory of trochaic template mapping and Demuth &
Fee’s (1995) prosodic hierarchy account. Both theories assume that
children build up the prosodic representation of words step-by-step, starting
with the smallest unit and ending with an adult-like representation. I argue
that both theories are problematic because they overgenerate certain
structures (e.g. level stress), but that the model of Demuth & Fee can better
account for the data presented so far.
This paper is organized as follows: since it is crucial in both theories,
paragraph 2 briefly introduces the basic assumptions of the prosodic
hierarchy. In paragraph 3, I will give a survey of Fikkert’s (1994) model of
stress development and Demuth and Fee’s (1995) model based on the
prosodic hierarchy. In paragraph 4, I discuss the problems arising with the
models and paragraph 5 concludes.
2. The prosodic hierarchy of words
The prosodic hierarchy up to the word level consists of four constituents.
The lowest element of the prosodic hierarchy is the mora (µ). Since there
are often no segmental slots in moraic models, the mora has a double
function as the unit of syllable weight and as the unique sub-syllabic
constituent. The moraic level is dominated by the syllabic level (σ), and
syllables are parsed into feet (F) at the foot level above. The highest unit is

148 Angela Grimm
the prosodic word (Wd) which directly dominates the foot level (see Figure
1):
Prosodic word (Wd)
Foot (F)
Syllable (σ)
Mora (µ)
Figure 4. The prosodic hierarchy (Selkirk, 1980)
Syllables differ with respect to the number of moras they contain. Light
syllables contain one mora, while heavy syllables contain at least two. The
tendency of languages to assign stress to heavy syllables is expressed by
the Weight-to-Stress-Principle (WSP). In a parametric approach to word
stress (cf. Hayes, 1995), languages either respect this principle (quantitysensitive
languages) or do not (quantity-insensitive languages).
The next constituent of the prosodic organization above the syllable
level is the foot. Ideally, the foot is binary branching which implies that it
should consist of two moras or of two syllables. Thus, a binary foot can be
monosyllabic if it contains two moras (e.g. ����� ‘duck’) or it can be
disyllabic if it consists of two syllables or two moras (e.g. ������ ‘papa’).
The head constituent of the foot receives stress.
The prosodic word is the domain of stress application. It can also
coincide with a single foot. Because the foot size is the smallest shape a
prosodic word can have, it is called Minimal Word. Many languages have
restrictions such that content words must not be smaller than the minimal
word. There is ample evidence that the minimal word restriction also
governs the shape of the early words in language acquisition (Demuth &
Fee, 1995; Demuth, 1996; Fikkert, 1994; Ota, 2001).
A very important principle of the prosodic hierarchy is the Strict Layer
Hypothesis (Selkirk, 1984) which demands that layers must not be skipped,
i.e. that a given prosodic constituent(n-1) is contained in the constituent(n)
immediately above. Furthermore, it requires that constituents have one and
only one head, which implies that there is always a difference in
prominence among the elements forming a given prosodic unit.

Prosodic Acquisition: a Comparison of Two Theories 149
3. The acquisition of word stress: two current models
3.1. Fikkert (1994)
Fikkert’s study of Dutch children is the most detailed research on stress
acquisition to date. Fikkert mainly focused on disyllabic words and argued
for the foot as the basic unit of development.
Although Fikkert’s model is based on Dutch, she claims that the
trochaic template is universal in child language since it is the only quantityinsensitive
foot in the typology of Hayes (1991). Thus, children should not
show sensitivity to syllable weight at the earliest stages of prosodic
acquisition. The postulation of a universal foot template implies that the
child always makes reference to the foot level in the word productions.
Consequently, it is a foot, not a syllable that is being truncated in forms like
below:
����� ��������
������� �����������
Example 1.
child form adult target gloss
‘ballon’
‘holiday’
Fikkert assumes that the output a child produces is directed by the mapping
of a melody template onto a trochaic template via prosodic circumscription.
Based on phenomena such as truncation, stress shift and epenthesis, four
different stages of prosodic development are postulated.
Stage 1
According to Fikkert, the child circumscribes the stressed syllable of the
adult form together with its segmental material and maps it onto a trochaic
template. The presumed representation of the child is given in Figure 2 (‘S’
denotes the prominent position and ‘W’ the non-prominent position within
the foot):

150 Angela Grimm
Wd
F
σS σW
�����
Figure 5. The prosodic representation at stage 1
Prosodic circumscription forces the child to divide the input into two parts,
the kernel (i.e. the stressed syllable) and the residue. In the mapping
process, the kernel (�����) is mapped onto the strong position in the
prosodic template. The residue (/��/) becomes truncated because there are
no empty positions in the template. The mapping onto the trochaic template
accounts for the fact that, if the result of prosodic circumscription is a
monosyllabic foot, sometimes a syllable is added to receive a disyllabic
output, for example ������� instead of ��������.
Stage 2
At stage 2, the child circumscribes a trochaic foot. Thus, if the prosodic
circumscription already results in a trochee as in ����������/ ‘holiday’, the
trochee remains unchanged in the output and appears as ��������. Words
consisting of more than a single foot are circumscribed differently. Fikkert
argues that the child selects the next stressed syllable to the left in addition
to the stressed final syllable. For instance, Dutch /������������ ‘crocodile’
should be realized as ���������� because the ultimate, main stressed
syllable and the antepenultimate, secondary stressed syllable are kept. The
disyllabic representation is then mapped onto the trochaic template
resulting in a trochaic pattern. Since the production template still consists
of one single trochaic foot, stress shifts to the initial syllable. The
representation of the child is depicted in Figure 3:

Prosodic Acquisition: a Comparison of Two Theories 151
Wd
F
σS σW
����� ����
������ �����
Figure 6. The prosodic representation at stage 2
Stage 3
At stage 3, the productions are extended to two feet. According to Fikkert,
the children have noticed that the target words can consist of more than a
single foot. She claims that her subjects realized two syllables of the target
word with equal prominence (level stress). However, her argument for the
level stress stage is rather weak: she stipulates that the children have to
produce two equally stressed feet because they are unable to realize stress
at word level.
The prosodic representation at stage 3 is depicted in Figure 4 below:
Wd
F F
σS σW σS σW
������ �����
Figure 7. The prosodic representation at stage 3
Since the trochaic foot still governs the productions, weak positions in the
template can be filled with extra syllables.

152 Angela Grimm
Stage 4
The representations are now adult-like. The word level stress has been
acquired and the child is able to operate at the level of the prosodic word.
3.2. Demuth & Fee (1995)
Demuth & Fee propose a more abstract approach which, although primarily
based on data of English acquiring children, aims to capture the prosodic
development universally. The basic assumption in Demuth & Fee’s model
is that prosodic development goes along the prosodic hierarchy (see Figure
1). In contrast to Fikkert, Demuth & Fee avoid the notion of prosodic
circumscription and trochaic template mapping. According to them,
sensitivity to the moraic structure of the mother tongue is already there
from the onset of word production on. They distinguish between the
following stages:
Stage 1
The first stage is characterized by sub-minimal (monomoraic) words. The
productions consist of a single CV-syllable and there are no vowel length
distinctions yet. Thus, the phonological representation of the words also is
CV.
Stage 2
At stage 2, children realize words of foot-size (Minimal Words). Stage 2 is
characterized by three successional sub-stages: at the beginning, the foot is
disyllabic as for example in ������ ‘papa’. Second, as soon as the child is
able to produce coda consonants the foot can also have a monosyllabic
form, e.g. ����� ‘duck’. Third, the vowel length distinction becomes
phonemic. The child is now aware of the fact that the stressed syllable of
Dutch ���������� ‘banana’ has to be realized with a long vowel ������, while
in �������� ‘giraffe’ the second vowel remains short (examples from Robin,
see Fikkert, 1994). Demuth & Fee assume a direct relationship between
distinctive vowel length and the appearance of coda consonants. Thus, a
CVV structure counts as sub-minimal, and a CVVCVV structure as
minimal as long as the child does not produce coda consonants.

Stage 3
Prosodic Acquisition: a Comparison of Two Theories 153
Beyond the minimal word stage, syllable structure can be more complex
and words can have a larger size than a single foot. This is also the stage
where the largest progress in the development of the word stress is
predicted. The child seems to become aware that feet have to be stressed
and that there are language-specific stress rules. Demuth & Fee do not
assume a trochaic template. However, they adopt Fikkert’s assumption of
an obligatory intermediate stage of level stress where two feet are produced
with primary stress.
At the end of stage 3, children acquire stress at the word level and they
realize one primary stress per word.
Stage 4
At the final stage, extrametrical (i.e. unfooted) syllables are permitted.
Children at this stage operate at the level of the prosodic word.
4. Discussion of the models
Although both models can explain a number of frequently observed
patterns like syllable deletions and word size restrictions, there are a
number of empirical and theoretical problems related with the models.
First, Fikkert and Demuth & Fee assume that the prosodic development
proceeds bottom-up, i.e. from a lower level of representation (the foot or
the mora) to the top of the prosodic hierarchy (the prosodic word). Children
invariably have to pass trough one stage before they can go to the next. For
example, multisyllabic words like ‘elephant’ or ‘crocodile’ have to show a
level stress pattern before they can be produced adult-like.
Fikkert explicitly points to that fact. Missing evidence in her data is
explained by the recording modalities or is due to the fact that a given stage
took a very short time. Demuth & Fee, in contrast, are not explicit with
respect to the ordering of the stages. However, they claim that prosodic
development proceeds along the prosodic hierarchy. Since in the prosodic
hierarchy one constituent strictly dominates the constituent below, stages
cannot vary with respect to their temporal order. According to the models,
the following realizations for /������������ ‘crocodile’ of Jule, a girl
acquiring German, should be chronologically impossible (data from my
own corpus):

154 Angela Grimm
Example 2.
child form age description
�����
��������
����������
(1;08,12)
(1;08,29)
the main stressed syllable is realised
a foot with final stress is realized
(1;10,14) level stress emerges
As the examples illustrate, level stress can occur after a finally stressed
variant of the target word was produced, contrary to the predictions of the
models. Such an acquisition order provides empirical evidence against level
stress as an obligatory component of prosodic development. Additional
empirical support comes from the data of English acquiring children
examined by Kehoe & Stoel-Gammon (1997) who also could not find a
systematic emergence of level stress.
Level stress as assumed in the models above is problematic also from a
grammatical point of view: the representation intended to create level stress
(see Figure 4 above) essentially violates the strict layer hypothesis because
the two feet are not correctly bounded into the prosodic word. The problem
is that the strict layer hypothesis never can be kept by such a representation
because there is no gradation in prominence at the word level. According to
prosodic theory, two equally stressed feet must not occur within a single
prosodic word:
*Wd
FS FS
σS σW σS σW
������ �����
Figure 8. The ill-formed representation of the prosodic hierarchy as implied by
Fikkert (1994) and Demuth & Fee (1995)
Both models remain vague with respect to the source of level stress: it is
unclear how the stages of level stress fit to the assumption that prosodic
development is directed by universal prosodic principles. Since they do not
discuss the possibility of a child-specific representation, the representation
according to the prosodic hierarchy should look like illustrated in Figure 6:

Prosodic Acquisition: a Comparison of Two Theories 155
Wd Wd
FS
FS
σS σW σS σW
������ �����
Figure 9. A prosodic representation that incorporates the requirements of the
prosodic hierarchy and that allows for level stress
The representation in Figure 6 admits the co-occurrence of two equally
stressed feet because every foot projects its own prosodic word. The
drawback is that this assumption is ad hoc. There is no motivation for
separating a single prosodic word like Dutch /������������ ‘crocodile’ into
two prosodic words. In addition, it is an open question which factors could
trigger the merging of the two prosodic words into a single one later.
Another problem is that the models described above are primarily based
on truncation patterns in multisyllabic words. This is critical from a
methodological point of view because it is presupposed that the truncation
of syllables is exclusively triggered by prosodic size restrictions. Recent
evidence, however, suggests that segmental properties of syllables can also
affect the truncation rate. For example, syllables with sonorant onsets seem
to be more prone to truncation than syllables with obstruent onsets (Kehoe
& Stoel-Gammon 1997).
A comparison of both models suggests that the predictions of the
template mapping model of Fikkert (1994) are sometimes too strong. Thus,
the prosodic hierarchy model of Demuth & Fee (1995) seems to be superior
because of its greater flexibility. First of all, it prevents Fikkert’s circular
process of assigning a trochaic structure via prosodic circumscription that
actually should be created by the foot template. Furthermore, the prosodic
hierarchy model allows for more variability in the productions of children.
For example, it allows for the co-occurrence of monosyllabic and disyllabic
feet in contrast to Fikkert’s model that only proposes disyllabic trochees for
a very long period of time. As the data of children acquiring English
suggest, there are doubts on Fikkert’s view that the disyllabic trochee is the
unique representation at the early stages (Kehoe & Stoel-Gammon, 1997;

156 Angela Grimm
Salidis & Johnson, 1997). Moreover, Fikkert predicts a systematic stress
shift to the left in disyllabic iambs, a pattern that still needs empirical
evaluation. It is also possible that stress shift is rather the result of a
complex interplay of factors like edge preferences, weight sensitivity and
segmental factors than of a simple template mapping mechanism. If this is
true, stress shift can be bidirectional to the left or to the right, depending on
the relative importance of the factors involved.
Fikkert’s model is more detailed than the model of Demuth & Fee. It is
at best elaborated for stage 1 and 2. With respect to the later stages she
remains somewhat inconsistent. For example, she strongly argues for the
foot as the relevant prosodic unit, but already at stage 2 the syllable, not the
foot, becomes the target of circumscription:
“[...] the child realises both syllables of the target word. However, stress
falls on the first syllable. The segmental material of both syllables of the
adult word is taken out and mapped onto the child’s trochaic template [...]”
(p. 210).
Fikkert also considers the possibility of circumscribing a foot. She
concludes that the children circumscribe syllables because the surviving
syllables do not constitute a foot in the adult word. But the examples she
presents (p. 211) do form two feet within a weight-sensitive model, with
each foot containing at least two moras (Example 3):
��������� �������������
���������� �������������
���������� �����������������
��������� �������������
Example 3.
child form adult target gloss
‘elephant’
‘pelican’
‘locomotive’
‘farm’
Fikkert cannot account for this fact because she exclusively assumes
weight-insensitive trochees at stage 2. Demuth & Fee’s model, in contrast,
would allow for the retention of the foot as the relevant unit since it
assumes sensitivity to syllable weight with the emergence of the foot
structure.
Demuth & Fee, in contrast, have problems to explain the stress shift to
the first syllable in the examples above for two reasons: first, recent
evidence suggests that the relationship between distinctive vowel length
and the emergence of coda consonants is not as categorial as they claim. In
an examination of Fikkert’s data, Salidis & Johnson (1997) found that,

Prosodic Acquisition: a Comparison of Two Theories 157
contrary to their English acquiring child, the vowel length was not
controlled by the Dutch children even if they correctly produced coda
consonants. If in turn, children cannot control vowel length appropriately,
they cannot assign two moras to a long vowel. The authors relate the
divergence between the languages to the impact of vowel quantity on the
stress pattern: in English, the long vowels in (C)VV(C) syllables count as
heavy and thus attract stress, contrary to Dutch which rather relies on the
open-closed distinction. In Dutch, a (C)VC syllable counts as heavy, while
a (C)VV does not. Thus, a learner of Dutch presumably does not rely on
vowel length as an indicator for stress, while it is crucial for a learner of
English to identify the relationship between vowel quantity and stress.
Second, given that the absence of the vowel length distinction is an artifact
of the investigation and children have mastered the vowel length
distinctions if they produce bimoraic feet. Then neither universal nor
language-specific constraints could account for the fact that the superheavy
finals lose their primary stress in favor of the less heavy ultimates because
Dutch follows the universal generalization that a (C)VCC (e.g. /����/) or a
(C)VVC (e.g. /����/) syllable is heavier than a (C)VV syllable (/��/,/���/).
The observation that Dutch children need more time to acquire vowel
length distinctions indicates that language-specific properties may influence
the prosodic representation in a more detailed way than assumed so far.
Thus, further empirical work is needed to shed light on the interplay of
universal principles and language-specific conditions in prosodic
development.
5. Conclusion
In the present paper, two models of prosodic development are introduced
and examined. As they evidence, the acquisition of word prosody largely
conforms to the prosodic hierarchy in such a way that universal prosodic
constituents as the foot or the mora govern children’s word productions.
This is essential in both models. However, it has turned out in the
discussion that there are empirical, theoretical and methodical
shortcomings. Common problems of both accounts are the absence of
empirical and theoretical motivation of level stress and the reliance on
truncations as the primary diagnostics of prosodic development.
In sum, the evidence so far rather supports the prosodic hierarchy model
of Demuth & Fee (1995) because it is more flexible than Fikkert’s template
mapping model.

158 Angela Grimm
References
Demuth, K. (1996). The prosodic structure of early words. In: J. Morgan & K.
Demuth (eds.) From signal to syntax: Bootstrapping from speech
to grammar in early acquisition. Lawrence Erlbaum Associates,
Hillsdale, N.J., 171-184.
Demuth, C. & Fee, J. (1995). Minimal words in early phonological
development. Ms., Brown University and Dalhousie University.
Fikkert, P.M. (1994). On the acquisition of prosodic structure. Holland Institute
of Generative Linguistics, Dordrecht,
Hayes, B. (1991). Metrical stress theory: principles and case studies. Ms,
UCLA.
Hayes, B. (1995). Metrical stress theory. Chicago University Press, Chicago.
Johnson, J. & Salidis, J.S. (1997). The production of minimal words: A
longitudinal case study of phonological development. Language
Acquisition, 6 (1): 1-36.
Kehoe, M. & Stoel-Gammon, C. (1997). The acquisition of prosodic structure:
An investigation of current accounts of children’s prosodic
development. Language, 73 (1): 113-144.
Ota, M. (2001). Phonological Theory and the Development of Prosodic
Structure: Evidence from Child Japanese. Available at
http://www.ling.ed.ac.uk/~mits/downloadables.shtml
Selkirk, E. (1980). The role of prosodic categories in English word stress.
Linguistic Inquiry, 11: 563-605.
Selkirk, E. (1984). Phonology and Syntax: The relation between Sound and
Structure. MIT Press, Cambridge, MA.

Base-Identity and the Noun-Verb Asymmetry in
Nivkh
Hidetoshi Shiraishi
1. Introduction
1.1. Background
Morphologically complex words often exhibit phonological similarities
with their morphologically related base forms which they are derived from.
In a number of cases, these similarities yield a marked phonological pattern
given the general rules or phonotactics of the language (Kenstowicz, 1996;
Burzio, 1997, 2002 etc.). In Optimality-Theory (OT), similarity between
existing words is captured by Output-to-Output (OO) correspondence
constraints (Burzio, 1996, 2002; Kenstowicz, 1996, 1997; Benua, 1997ab;
Ito and Mester, 1997; Steriade, 2000 etc.). The marked phonological
pattern arises when similarity between words takes priority over the
canonical phonology of the language. OT expresses this situation by
ranking OO-correspondence constraints above phonological markedness
constraints. OO-correspondence constraints evaluate the output candidates
and select the one which is most similar to the base.
Since the base plays a crucial role in computing the phonology of its
derivatives, it is important to identify the correct surface form as the base.
Many authors have observed that OO-constraints have access to the base
only if the latter occurs as an independent word (Kenstowicz, 1996; Benua,
1997a; Ito and Mester, 1997). 1 Consider the s-voicing observed in the
northern dialects of Italian. In these dialects, s and z are in complementary
distribution. Z appears intervocalically, when the flanking vowels belong to
the same phonological word (examples from Kenstowicz, 1996: 373-374).

160 Hidetoshi Shiraishi
1.1
a. a[z]ola ‘button hole’
a[z]ilo ‘nursery school’
ca[z]-a ‘house’
ca[z]-ina ‘house - diminutive’
b. lo [s]apevo ‘I knew it’
telefonati [s]i ‘having called each other’
The distribution of s-voicing in lexical items containing a prefix is more
complicated. When the target precedes the boundary, s-voicing applies
(1.2a). But when the target follows the boundary, s-voicing may or may not
apply, even if the structural description of s-voicing is met (1.2b, c).
1.2
a. di[z]-onesto ‘dishonest’
di[z]-ugale ‘unequal’
b. re-[z]istenza ‘resistance’
pre-[z]entire ‘to have a presentiment’
c. a-[s]ociale ‘asocial’
bi-[s]essuale ‘bisexual’
pre-[s]entire ‘to hear in advance’
The unexpected blocking of s-voicing in 1.2c is in sharp contrast with the
items in 1.2b where z surfaces intervocalically, following the phonological
norm of the language. Nespor and Vogel (1986) pointed out that the crucial
difference between the items in 1.2b and 1.2c lies in the lexical status of the
stem to which the prefix is attached; in 1.2c the stem occurs as an
independent word (sociale, sessuale, etc.) whereas in 1.2b it does not
(*sistenza, etc.). Following this view, Kenstowicz (1996) claimed that there
is a lexico-morphological pressure from the independently occurring stem
to surface its derivative as similar as possible. The presence of such an
independently occurring immediate constituent is thus crucial in computing
the phonology of a morphologically complex item. Kenstowicz dubbed this
generalization Base-Identity; the base forces its derivative to be formally as
similar as possible in order to “improve the transparency of morphological
relationships between words and enhance lexical access” (Kenstowicz,
1996: 372).

Base-Identity and the Noun-Verb Asymmetry in Nivkh 161
1.3 Base-Identity: Given an input structure [X Y] output candidates are
evaluated for how well they match [X] and [Y] if the latter occur as
independent words. (Kenstowicz, 1996: 372)
The languages in East Asia provide an interesting test for this
generalization. Languages as Korean or Japanese show a systematic
difference in the composition of verbs and nouns; while verbal stems
always surface with a morphological extension, nominal stems may surface
without such an extension. This means that complex words formed from a
nominal stem always have an independently occurring base to which they
phonologically should conform, whereas verbal derivatives lack such a
base and hence should not show such conformity. This prediction is borne
out in Korean in which derivatives of nominal and verbal stems are subject
to different phonology (Kenstowicz, 1996. See section 2.3 below.). In this
paper, I discuss another language of East Asia, Nivkh, which also has an
asymmetric composition of nouns and verbs like Korean and Japanese. I
will focus on two phonological phenomena, Consonant Alternation and
Final Fricative Devoicing and show that both phenomena exhibit
asymmetries between nominal and verbal phonology. I will discuss each
case in detail and argue that Base-Identity is the driving force of these
asymmetries.
The article is organized as follows. I will start with a descriptive sketch
of Consonant Alternation (section 2.1) and then illustrate the exceptional
behavior of nominal stems as a case of noun-verb asymmetry (section 2.2).
While most previous works, including my own, somehow stipulated the
asymmetric behavior of nominal and verbal stems, I will argue that Base-
Identity provides a superior analysis which is free from such a stipulation.
Section 3 discusses the second phenomenon, Final Fricative Devoicing. I
will illustrate the asymmetric behavior of fricative-final nominal and verbal
stems when followed by a suffix. The pattern of asymmetry is as in CA:
while verbal phonology is subject to canonical phonology, nominal
phonology is not. Section 4 concludes.
1.2. About Nivkh
Nivkh (also called Gilyak) is an isolated language spoken by the people of
Nivkh, who live on the island of Sakhalin and in the lower reaches of the
Amur River in the Russian Far East. The language has four dialects and the

162 Hidetoshi Shiraishi
major discrepancy is between the Amur dialect, spoken in the Amur area on
the continent and the west coast of north Sakhalin, and the Sakhalin dialect
spoken in the east coast of Sakhalin. Nivkh is listed in the UNESCO Red
Book on endangered languages as being seriously endangered. According
to the census of 1989, the percentage of speakers is 23, 3% of the total
population of 4,681. 2 This article concerns the phonology of the Amur
dialect spoken by the continental Nivkh. All the examples are from the
following sources, unless otherwise mentioned: Krejnovich (1937), and
Saveleva and Taksami (1970).
2. Consonant Alternation
2.1. A descriptive sketch
I will first outline the segmental inventory of Nivkh.
2.1 Consonantal inventory of Nivkh
(I) aspirated plosives p� t� c� k� q�
(II) non-aspirated plosives p t c k q
(III) voiceless fricatives f r� s x �
(IV) voiced fricatives v r 3 z � �
nasals m n � �
lateral l
glides j h
2.2 Vowels
i � u
e o
a
Consonant Alternation (henceforth CA) is a phonological process which
changes the feature [continuant] in obstruents when they are placed in
certain phonological and morphosyntactic contexts. Descriptively, CA
consists of two processes: spirantization, in which a plosive changes to a
fricative, and hardening, in which a fricative changes to a plosive.
Laryngeal features are also relevant since aspirated plosives only alternate

Base-Identity and the Noun-Verb Asymmetry in Nivkh 163
with voiceless fricatives and non-aspirated plosives with voiced fricatives,
i.e. the alternation is strictly between the obstruents of row (I) and (III), or
4, 5
(II) and (IV).
2.3 Spirantization: (I) > (III), (II) > (IV)
a. (I) > (III) mac�a [r�]om (< t�om) ‘fat of a seal’
seal fat
c�ol�i [�]os (< q�os ) ‘neck of a
reindeer neck reindeer’
b. (II) > (IV) p�eq [v]��x (< p��x ) ‘chicken soup’
chicken soup
mac�a [z]us (< cus) ‘meat of a seal’
seal meat
2.4 Hardening: (III) > (I), (IV) > (II)
a. (III) > (I) c�x�f [q�]a- (< �a-) ‘to shoot a bear’
bear shoot
cus [t�]a- (< r�a-) ‘to bake meat’
meat bake
b. (IV) > (II) tux [k]e- (< �e-) ‘to take an axe’
axe take
p�n�nx [t]�u- (< r�u-) ‘to teach one's
one's sister teach sister’
The phonological contexts of spirantization and hardening are in
complementary distribution. Spirantization takes place when the target
(plosive) follows a vowel, a glide, or a plosive (2.5). There is no
spirantization when the target follows a fricative or a nasal (2.6).
2.5 Spirantization Preceding segment
Vowel mac�a [r�]om ‘fat of a seal’
Glide k��nraj [r�]om ‘fat of a duck’
k��nraj [v]��x ‘duck soup’
Plosive �t [r�]om ‘fat of a species of
duck’
amsp [v]��x ‘soup of a species
of seal’

164 Hidetoshi Shiraishi
2.6 No spirantization
Fricative c�x�f t�om ‘bear fat’
c�x�f p��x ‘bear soup’
Nasal k�e� t�i ‘sun ray’
rum d�f ‘Rum(person)’s house’
On the other hand, hardening occurs when the target (fricative) follows
either a fricative or a nasal (2.7). When a segment other than fricative
precedes the target, hardening does not occur (2.8).
2.7 Hardening Preceding segment
Fricative cx�f [q�]a- (< �a-) ‘to shoot a bear’
lovr� [c]osq-(< zosq-) ‘to break a spoon’
Nasal qan [d]�u- 6 (

Base-Identity and the Noun-Verb Asymmetry in Nivkh 165
whereas hardening activates when a fricative is in the input. In the past,
many approaches have overlooked this generalization and described the
rules as if they had independent structural goals. This is not the case.
Let us now move to the morphosyntactic conditioning. CA targets a
segment at the left edge of a derived morphosyntactic unit in the presence
of a preceding segment. CA applies cyclically to every left edge of a
morpho-syntactic unit until the maximal projection (NP, VP) is reached.
2.10 Means of derivation
Prefixation p�-[r�]u (< t�u) ‘one’s own sledge’
REF-sledge
Postposition t��x-tox ‘towards the top’
top-ALL
tu-rox ‘towards a lake’
qan-dox ‘towards a dog’
Reduplication t�k[r�]�k- ‘to be silent’
(Sakhalin dialect, Hattori, 1962: 107)
NP formation mac�a [r�]om ‘fat of a seal’
VP formation cx�f [q�]a- (< �a-) ‘to shoot a bear’
On the other hand, CA never targets segments in a non-derived
environment, nor does it apply across XP boundary, as shown in 2.11 and
2.12, respectively.
2.11 CA does not apply in non-derived environment
utku *ut[�]u ‘man’
n��s *n��[c�] ‘teeth’
e�l�
2.12 No CA across XP boundary (subject-predicate)
r�o- ‘The child holds (something)’
= [NPe�l�] [VPr�o-] (‘child’ is subject)
Example 2.13 below differs minimally from example 2.12 above with
respect to the application of CA. In the former, CA applies since the noun
is the object of the following predicate. Thus these two words form a VP,
differing minimally from example 2.12.

166 Hidetoshi Shiraishi
2.13
e�l� [t�]o- ‘(Someone) holds the child’
= [VP[NPe�l�][V t�o-]] (‘child’ is object)
2.2. The spirantization – hardening asymmetry
There is one environment in which the regular pattern of CA as depicted
above fails to apply. Nouns beginning with a fricative never undergo
hardening. In such a case, the structural goal of CA (2.9) is not achieved. In
this context the otherwise illicit fricative-fricative or nasal-fricative
sequence appears.
2.14
a. t�ulv vo *t�ulv [b]o ‘winter village’
winter village
b. c���r vox *c���r [b]ox 'a hill covered with grass'
grass hill
c. t�f r�� *t�f [t�]� ‘entrance door’
house door
d. t�e� vaqi *t�e� [b]aqi ‘coal box’
coal box
Previous works have either described this context as an exception to CA, or
did not discuss it. In most cases, these works simply stipulated that a) nouns
do not undergo hardening, or alternatively b) only transitive verbs undergo
hardening. Once stated as a condition this way, the application of hardening
to nouns can indeed be avoided. However, adding such a condition (in
either form) to a phonological rule pairs prosodic phonology with specific
category labels (transitive verb, noun), which is unlikely to occur in natural
languages (Nespor and Vogel, 1986; Selkirk, 1986 etc.). 7 But most
critically, it is explanatorily unsatisfying; why should hardening be
restricted to transitive verbs (or alternatively, why should nouns be an
exception to hardening)? No literature provides a satisfactory answer to this
question.
The tacit assumption prevailing in the previous works is that the input to
CA is the citation form, i.e. the form that appears in isolation. Following
this assumption, the transitive verbs ought to undergo hardening since they
initiate with a fricative in the citation form. However, there is no a priori

Base-Identity and the Noun-Verb Asymmetry in Nivkh 167
reason that the citation form should be the underlying form. In Shiraishi
(2000), I defended the position that the citation form of these transitive
verbs cannot be the underlying form, if we want to advocate a
phonologically plausible analysis for the observed spirantization-hardening
asymmetry. The lack of hardening in nouns could be interpreted as
evidence that CA consists solely of spirantization, without hardening. I
argued that transitive verbs of Nivkh initiate with a plosive at the
underlying level, instead of a fricative that appears in the citation form.
Initiating with a plosive, transitive verbs now undergo spirantization in the
8, 9
same way as nouns do.
2.15
Previous analyses Shiraishi (2000)
VP 'shoot a NP 'bird soup' VP 'shoot NP 'bird
bear'
a bear' soup'
Underlying
form
cx�f �a- p�eq p��x cx�f q�a- p�eq p��x
Spirantization not
applicable
p�eq [v]��x blocked p�eq [v]��x
Hardening cx�f [q�]a- not applicable
Surface form cx�f q�a- p�eq v��x cx�f q�a- p�eq v��x
The analysis in Shiraishi (2000) leaves hardening out of the list of
phonological processes; nouns do not undergo hardening since there is no
hardening in the phonology of the language.
2.16
Previous
analyses
Shiraishi (2000)
Underlying
form
t�ulv vo t�ulv vo
Spirantization not applicable not applicable
Hardening t�ulv [b]o
Surface form �t�ulv bo t�ulv vo �: incorrect output
This analysis is free from category-specific specification in the structural
description of the rule, which was inevitable in the previous analyses.
Although this analysis explains nicely why fricative-initial nouns never
undergo hardening in Nivkh, it is not without problems. First, it

168 Hidetoshi Shiraishi
manipulates the underlying form of a specific lexical category (transitive
verb) in order to explain phonologically exceptional behavior. Although
such a 'prespecification' at the underlying level is not an uncommon way to
approach phonological exceptions (cf. Inkelas, Orgun and Zoll, 1997
amongst others), such an approach does not explain why only this
particular class of words needs to undergo such manipulation. Since
prespecification puts unpredictable information into the lexicon, it is a
strong descriptive device which leaves little space for phonological
generalizations. Contrary to what seems to be the case at first glance, the
analytical gain of Shiraishi (2000) from previous analyses is not so
obvious. One may ask correctly what the difference between the two
analyses is, which claim that a) nouns are exceptions to hardening
(previous analyses) or b) transitive verbs undergo spirantization because
they initiate with plosives underlyingly (Shiraishi 2000). In other words, it
remains an arbitrary choice that only transitive verbs, and not other
categories, undergo prespecification.
Secondly, the relationship between the underlying form and the citation
form is obscured in transitive verbs. By positing a form other than the
citation form as the underlying form, the citation form would always be
derived from the underlying form by some morphological operation. That
is, Shiraishi (2000) created asymmetry between the morpholexical make-up
between nominal and verbal stems.
2.17
Nominal stem Verbal stem
Underlying form p��x q�a-
Surface form p��x �a-
In fact, this asymmetry describes the historical path of derivation of
transitive verbs (Jakobson, 1957; Austerlitz, 1977). On synchronic grounds,
however, it is highly doubtful whether such a morphological operation can
be justified.
In the next section I propose an alternative approach to the
spirantization-hardening (or noun-transitive verb) asymmetry, which makes
use neither of prespecification nor of information about category labels.
Instead, I will argue that correspondence relation between output forms
plays a decisive role in distinguishing the phonological behavior of the two
groups. Once stated this way, nothing ought to be stipulated in order to

Base-Identity and the Noun-Verb Asymmetry in Nivkh 169
derive the surface form; this follows naturally from the phonological
principles of the language.
2.3. Noun-verb asymmetry as Base-Identity
In Nivkh, verbal and nominal stems differ from each other in one crucial
morphological aspect; verbal stems should always end in a morphological
extension but nominal stems do not. Or put differently, verbal stems never
surface in isolation, whereas nominal stems do. This means that bare verbal
stems cannot function as citation forms. Usually, the form with an
infinitival suffix (-d�, -t�) provides the citation form.
2.18
2.19
Stem /�a/ ‘to shoot~’ /r�o/ ‘to take’
Infinitive �a-d� r�o-d�
(citation form)
‘when~’ �a-�an r�o-�an
/vo/ ‘village’ /�ota/ ‘town’
Citation form vo �ota
Allative vo-rox �ota-rox
As mentioned in section 1, independent forms often exercise special
influence on the realization of morphologically related forms in derived
contexts. For instance, in certain varieties of English the existence of the
form condense guarantees that the vowel of the second syllable in the
morphologically related word condensation does not reduce to a schwa.
2.20
co�nd[�]nsa�tion co�mp[�]nsa�tion
cond[��]nse co�mp[�]nsa�te
Phonology would expect the unstressed vowel of condensation to surface
with a schwa, as is the case with the structurally similar compensation. The
usual explanation for this asymmetry is that the vowel reduction in
condensation is blocked by virtue of the existence of the morphologically
related form condense, which appears with a full vowel [�] (Chomsky and

170 Hidetoshi Shiraishi
Halle, 1968: 110-116). On the other hand, compensation lacks such a
morphologically related form with a full vowel. Hence unstressed vowel
reduces to a schwa, following the phonological norm of the language.
Another example comes from Korean. In Korean a stem-final consonant
cluster surfaces only when it is followed by a vowel-initial suffix. In
combination with a consonant-initial suffix, the cluster is simplified to a
single consonant (Kenstowicz, 1996: 375).
2.21
Stem /kaps/ ‘price’ /talk/ ‘chicken’
Citation form kap tak
Nominative kaps-i talk-i
Comitative kap-k'wa tak-k'wa
In the speech of younger generation of Seoul, however, simplification overapplies
to contexts where vowel-initial suffix follows the stem.
2.22
Nominative kap-i tak-i
Interestingly, this overgeneralization does not apply to verbal stems. Here
the consonant cluster surfaces.
2.23
�ps-�ss-� palk-�ss-�
Stem /�ps/ ‘not have’ /palk/
‘be bright’
Past-informal (*�p-�ss-�)
(*pak-�ss-�)
Non-past-formal �p-t'a pak-t'a
Kenstowicz analyzed the absence of the cluster simplification in verbal
stems to be due to a lack of corresponding citation forms. As in Nivkh,
verbal stems in Korean never appear in isolation; they should always
appear with an inflectional ending. In contrast, nominal stems are free to
appear without any inflectional ending, so they exercise strong influence on
the realization of their derivatives. Verbal stems, on the other hand, surface
with consonant clusters since there are no isolated counterparts which
forces conformity to it. This is an instance of Base-Identity, which requires
forms in derived contexts to be formally similar to the base. This is the

Base-Identity and the Noun-Verb Asymmetry in Nivkh 171
generalization captured in the Base-Identity constraint of Kenstowicz (1.3),
repeated below.
2.24 (=1.3) Base-Identity: Given an input structure [X Y] output
candidates are evaluated for how well they match [X] and [Y]
if the latter occur as independent words. (Kenstowicz, 1996:
372)
We can account for the noun-verb asymmetry in Korean using Base-
Identity as a high-ranked constraint. By ranking Base-Identity above a
faithfulness constraint which prohibits deletion of a segment in the input
(MAX), nominal stems surface with a single consonant in concordance
with the base.
2.25
constraints →
/kaps+i/ base: kap
candidates ↓
Base-Identity *CLUSTER MAX
kapsi *!
�kapi *
Base-Identity is vacuously satisfied in verbal stems. Since there is no base
to which verbal stems should conform, verbal stems exhibit canonical
phonology. Consonant clusters surface only if a vowel-initial suffix
follows, elsewhere they are simplified. A phonological markedness
constraint *CLUSTER penalizes every output candidate containing a triconsonantal
cluster.
2.26
constraints →
/�ps+�ss+�/ base: ø
candidates ↓
��ps-�ss-�
Base-Identity *CLUSTER MAX
�p-�ss-� *!

172 Hidetoshi Shiraishi
2.27
constraints →
/�ps-t'a/ base: ø
candidates ↓
Base-Identity *CLUSTER MAX
�ps-t'a *!
��p-t'a *
The noun-verb asymmetry of hardening in Nivkh is strikingly similar to the
case of Korean. As in Korean, verbal stems of Nivkh are not allowed to
surface in isolation; they always require a morpho-syntactic extension
(2.18). This is in contrast to nominal stems, which may surface in isolation
(2.19). The difference is reflected directly in their phonological behavior;
verbal stems undergo hardening, nominal stems do not. In the next section I
will show how this analysis formally works.
2.4. Base-Identity blocks hardening
I assume that the phonological markedness constraint that induces
hardening to be the Obligatory Contour Principle (OCP) [fric]. 10 OCP [fric]
prohibits adjacent fricatives. Base-Identity, as defined in the previous
section, prefers output candidates which are similar to the base. With the
ranking Base-Identity >> OCP [fric], we obtain the desired output;
hardening does not apply to nominal stems.
2.28
constraints →
/tulv vo/ base: vo
candidates ↓
Base-Identity OCP [fric] IDENT [cont]
�tulv vo *
tulv bo *! *
Base-Identity is satisfied vacuously in verbal stems since they lack a base.
Being free from Base-Identity, an initial fricative now hardens to a plosive
in order to circumvent an OCP violation.

2.29
constraints →
/c�x�f �a-/ base: ø
candidates ↓
Base-Identity and the Noun-Verb Asymmetry in Nivkh 173
Base-Identity OCP IDENT [cont]
c�x�f �a- *!
�c�x�f [q�]a- *
Since Base-Identity refers to the base and not to the input, this ranking
always derives the correct output no matter of the input value. This is
illustrated in the tableau below in which the verbal stem initiates with a
plosive in the input (cf. Shiraishi, 2000).
2.30
constraints →
/c�x�f q�a-/ base: ø
candidates ↓
Base-Identity OCP IDENT [cont]
�c�x�f q�ac�x�f
[�]a- *! *
The present analysis correctly derives the observed output no matter of the
input. There is thus no prespecification, in which input strings are fixed to
take a particular form. Nor does it make use of information of category
labels, a condition that was inevitable in previous descriptions in order to
let hardening apply appropriately. The current analysis makes a totally
different claim. There is no exception to the hardening rule (nominal
stems), nor should the specific undergoer (verbal stems) be prespecified at
the underlying level. Rather, the asymmetry of nominal and verbal stems
follows from the existence of a base, which is an independent fact of the
language. By making use of such morpho-lexical information, the current
analysis accounts for the noun-verb asymmetry without appealing to
language-specific stipulations.
3. Final Fricative Devoicing
Base-Identity plays a crucial role in another phonological phenomenon of
Nivkh. In this section, I will discuss such a case.

174 Hidetoshi Shiraishi
3.1. Distribution of laryngeal features
Like Danish, a full contrast of laryngeal features of Nivkh obstruents is
realized only at the stem-initial position, which is the most prominent
position as in many other languages (cf. Beckman, 1996). In other
positions, laryngeal features do not exercise a phonemic contrast and the
feature value at the surface level is predictable from the context (Jakobson,
1957: 83). In principle, non-prominent (stem-medial and final) positions
only allow non-aspirated plosives and voiced fricatives. Aspirated plosives
and voiceless fricatives, on the other hand, are excluded from these
positions. Following Jakobson (1957), I will call them the lenis and fortis
series, respectively.
3.1
3.2
Lenis obstruents non-aspirated plosives : p t c k q
voiced fricatives : v r z � �
Fortis obstruents aspirated plosives : p� t� c� k� q�
voiceless fricatives : f r s x �
pal ‘forest’ �t�k ‘father’
p�al ‘floor’ �k�n ‘mother’
ra-d� ‘to drink’ ova ‘flour’
r�a-d� ‘to bake’ muvi ‘porridge’
eri ‘river’
There are two exceptional contexts in which a voiceless fricative appears in
a non-prominent position: i) when preceding a plosive, and/or ii) before an
I[ntonational] P[hrase] boundary (Jakobson, 1957: 83).
3.3
a. esqa-d� ‘to hate’
taft� ‘salt’
kins ‘evil spirit’
kins k�u-d� ‘to kill an evil spirit’
c�x�f ‘bear’
c�x�f k�u-d� ‘to kill a bear’
als ‘berry’
als p�e- ‘to pick berries’

. nivx ‘human’
erx ‘to him/her’
Base-Identity and the Noun-Verb Asymmetry in Nivkh 175
The examples in 3.3b indicate that it is only the absolute final position that
matters; the fricative second from the right appears as voiced. In Nivkh,
there are no words ending in consecutive voiceless fricatives, indicating
that voicelessness is required only for the very last fricative in an IP. I
assume this to be due to a restriction which I will call Final Fricative
Devoicing (FFD). FFD targets every final fricative within an IP.
Stem-final voiceless fricatives appear as voiced, however, as soon as the
above-mentioned conditions are removed. Thus if a stem-final fricative is
embedded in an IP, i.e. not final in the domain, and if it is not adjacent to a
plosive it becomes voiced (3.4a). This is in concordance with the
phonotactics of stem-medial fricatives which are always voiced (3.4b)
unless adjacent to a plosive. This distribution is not surprising since stemmedial
fricatives are expected not to coincide with an IP-boundary.
3.4
a. [kinz it-]I ‘go insane’
[c�x�v l�j-]I
'to kill a bear'
[alz �a-]I
‘to pick berry’
b. ezmu- ‘to like~’
urla ‘good’
pa�la ‘red’
Outside of these two contexts, only lenis obstruents appear in nonprominent
positions. Apparently, lenis obstruents have more distributional
freedom than fortis obstruents, indicating their unmarked status in the
phonology of Nivkh. Since non-prominent positions are predictably
occupied by lenis obstruents, I assume that obstruents in these positions are
unspecified for laryngeal features in the underlying form. Unless contextsensitive
requirements contravene, obstruents without laryngeal
specifications surface as lenis, the unmarked obstruent of the language.

176 Hidetoshi Shiraishi
3.2. Base-Identity in suffixation
Having discussed the unmarked nature of the lenis obstruents, we are now
ready to look at the way FFD interacts with Base-Identity. Such a case
arises when a suffix attaches to a fricative-final stem.
Like stem-medial and final positions, the initial obstruent of a suffix
does not exhibit a laryngeal contrast, indicating that it is a non-prominent
position. Except for a few exceptional cases, only lenis obstruents are
allowed. 11
3.5
-tox/rox/dox allative (case suffix)
-�u/gu/ku plural
-t�/d� infinitive
-gu/ku causative
When affixed to a stem, the redundant [+voice] specification of the stemfinal
segment spreads to the initial obstruent of the suffix.
3.6
‘to drink-INF’
ra-d�
‘big-INF’
pil-d�
‘to walk-INF’
amam-d�
‘to harness-INF’
ifk-t�
‘to bind-INF’
jup-t�
‘to help-CAU-INF’
ro-gu-d�
‘to do-CAU-INF’
l�t-ku-d�
c�am-gu ‘shaman-PL’
c�am-dox ‘shaman-ALL’
There is an interesting discrepancy between fricative-final nominal and
verbal stems in this context; following a verbal stem, the initial segment of
a suffix is always voiced (3.7a), while following a nominal stem, it is
always voiceless (3.7b).
fuv-d�
i�-d�
t�v�-d�
3.7
a. ‘to blow/to saw-INF’
‘to kill-INF’
‘to go inside the house-INF’

Base-Identity and the Noun-Verb Asymmetry in Nivkh 177
jar-d�
roz-gu-d�
t�mz-gu-d�
b. kins-ku
‘to feed-INF’
‘to divide-CAU-INF’
‘drop-CAU-INF’
‘evil spirit-PL’
c�x�f-ku ‘bear-PL’
or�r�-ku ‘Uilta-PL’
t�f-tox ‘house-ALL’
ti�r�-tox ‘wood-ALL’
The reason of this discrepancy is not immediately clear. In particular, the
final voiceless fricative of nominal stems is a mystery. Being affixed by a
suffix, it is no longer in the context of FFD, so nothing prevents it from
appearing in the unmarked voiced fricative. In fact, this is the case with
verbal stems; final fricatives of verbal stems are systematically voiced
(3.7a). The other context-sensitive requirement, namely, the precedence to
a plosive cannot be the reason either since these suffixes have a voiced
variant, which surfaces when following a (redundant) [+voice] segment
(3.6, 3.7a). The derivatives of verbal stems in 3.7a show that the initial
plosive of these suffixes can accommodate a (preceding) voiced fricative,
unlike plosives in a stem. But in fact, this option is not adopted in nominal
stems. In short, these context-sensitive requirements cannot explain the
different behavior of final-fricatives in nominal and verbal stems.
Under Base-Identity, however, such a discrepancy is explicable. Recall
that nominal and verbal stems have different morpho-lexical compositions.
Nominal stems can surface without any morphological ending, making the
last fricative target of FFD. In contrast, final fricative of a verbal stem is
always followed by a morphological extension, making it irrelevant to
FFD. Since Base-Identity claims that derivatives should phonologically
conform to the base, nominal derivatives conform to their base, which ends
in a voiceless fricative (due to FFD). This is not the case, however, for
verbal stems since they have no base and therefore do not underlie such
pressure. As a consequence, verbal stems undergo canonical phonology and
fricatives in non-prominent positions do appear as lenis, the unmarked
obstruents of the language.
Finally, it is important to note that reference to laryngeal specifications
using Input-to-Output correspondence constraints is not a viable option in
this context. Recall that there is no laryngeal contrast in stem-final position
in Nivkh. A phonological theory which minimizes the specification of
predictable features in underlying representations, which is the one adopted

178 Hidetoshi Shiraishi
here, makes it impossible for Input-to-Output constraints to refer to the
voiceless status of stem-final fricatives. 12 Thus their voicelessness should
come from somewhere else. According to the current analysis it originates
from the base, the independently occurring isolated form.
4. Conclusion
In this paper I have discussed phonological asymmetries between nominal
and verbal stems of Nivkh, as observed in two phonological phenomena
CA and FFD. Though the asymmetries themselves look very different on
the surface, this article has made explicit that they are subject to a common
generalization, Base-Identity. Given the asymmetric composition of nouns
and verbs, Base-Identity makes two predictions: i) nominal and verbal
derivatives exhibit different phonological patterns, and ii) it is the nominal
stem which exhibits the non-canonical phonology given the strong pressure
from the base. Both predictions were borne out in the phonological
phenomena discussed above. The base plays a decisive role in computing
the phonology of nominal and verbal derivatives in both CA and FFD. As
for CA, the current analysis correctly predicts that nominal derivatives
accommodate the otherwise illicit segmental sequence (fricative-fricative,
nasal-fricative), while verbal derivatives do not. This analysis is superior to
previous accounts since it makes no direct use of the notion of exception,
which was inevitable in previous works. Rather, the suggested analysis
relates the asymmetry in phonology to the compositional asymmetry
between nouns and verbs.
As for FFD, nominal derivatives showed conformity to their base, in
ending in a voiceless fricative. Verbal stems on the other hand, do not show
such conformity since they lack a base. Unlike nominal derivatives, the
stem-final fricatives of verbal derivatives appear as lenis, following the
canonical phonology of Nivkh. Base-Identity provides us with the
mechanism underlying the noun-verb asymmetry, and it correctly predicts
their phonological behavior with respect to the canonical phonology of the
language.

Acknowledgements
Base-Identity and the Noun-Verb Asymmetry in Nivkh 179
I would like to thank Dicky Gilbers, Angela Grimm, Maartje Schreuder,
Jeroen van de Weijer and the audiences of ULCL Phonology meeting at
Leiden (27-05-2003) and TABU dag (20-06-2003, Groningen) for
comments on parts of this article. I bear all responsibility for errors.
Notes
1
“…identity effects will come into play only to the extent that the immediate
constituents composing the complex structure constitute independently
occurring outputs…(Kenstowicz 1996: 373)”, “The base of an OOcorrespondence
relation is a licit output word, which is both morphologically
and phonologically well-formed (Benua 1997a: 29)”, “The bound form of a
stem is segmentally identical with its corresponding free form (Ito and Mester
1997: 431)”.
2
See www.let.rug.nl/~toshi/ for more information.
3
The rhotic r of Nivkh is classified here and elsewhere in the literature (e.g.
Trubetzkoj 1939) as a voiced fricative since it patterns as such in the CA
system. Its voiceless r� counterpart is an apical trill containing portions without
vocal cord vibration (Ladefoged and Maddieson 1996: 236).
4
Regarding this nature of CA, one may postulate a single laryngeal feature
(rather than two) for both plosives and fricatives, e.g. [+spread glottis] for both
aspirated plosives and voiceless fricatives. Such an analysis is proposed by
Jakobson (1957) and Blevins (1993). See also section 3 below.
5
Segments that underwent CA are put in square brackets. Abbreviations are:
ALL= allative, asp= aspiration, I=Intonational phrase, INF=infinitive, NP =
noun phrase, PL= plural, VP = verb phrase, XP = maximal projection.
6
The alternation (r >) t > d is due to post-nasal voicing.
7
CA exhibits aspects of prosodic phonology (I am using this term to contrast
with lexical phonology); it is sensitive to pause insertions and to speech rate. I
would classify it as a P-structure rule in the terminology of Selkirk (1986). Pstructure
rules exhibit phonological properties of prosodic phonology, yet they
are sensitive to syntactic bracketing (Selkirk 1986).
8
This line of analysis has antecedents. Amongst them are: Kenstowicz and
Kisserberth (1979), Rushchakov (1981), Kaisse (1985), and Blevins (1993).
Interestingly, Lev Shternberg, the pioneer of Nivkh study, assumed plosiveinitial
forms to be the input to transitive structures, as well (Shternberg 1908).

180 Hidetoshi Shiraishi
9
Spirantization and hardening are not ordered relative to each other in the
tableau below.
10
Post-nasal context requires different markedness constraint but I omit it from
the discussion below. See Shiraishi (2000) for details.
11
Following a velar or a uvular plosive, the initial velar of a suffix appears as [x],
spirantizing the former at the same time: �t�x-xu

Base-Identity and the Noun-Verb Asymmetry in Nivkh 181
Ito, J. and A. Mester (1997). Correspondence and Compositionality: The Gagyo
Variation in Japanese Phonology. In: I.Roca (ed.), 419-462.
Jakobson, R. (1957). Notes on Gilyak. Roman Jakobson. Selected Writings II.
Word and language. Mouton, The Hague and Paris, 72-102.
Kaisse, E. (1985). Connected Speech. Academic Press, Orlando.
Kenstowicz, M. (1996). Base-Identity and Uniform Exponence: Alternatives to
Cyclicity. In: J. Durand and B. Laks (eds.), 365-395.
Kenstowicz, M. (1997). Uniform exponence: Exemplification and extension.
In: V. Miglio and B. Moren (eds.), 139-155.
Kenstowicz, M. and C. Kisserberth. (1979). Generative Phonology: description
and theory. Academic Press, New York.
Krejnovich, E. (1937). Fonetika nivxskogo (giljackogo) jazyka [Phonetics of the
Nivkh (Gilyak) language]. Uchpedgiz, Moskva - Leningrad.
Ladefoged, P. and I. Maddieson. (1996). The Sounds of the World’s Languages.
Blackwell, Oxford.
Miglio, V. and B. Moren (eds.) (1997). University of Maryland Working
Papers in Linguistics, vol.5.
Nepor, M. and I. Vogel (1986). Prosodic Phonology. Foris, Dordrecht.
Roca, I. (ed.) (1997). Derivations and Constraints in Phonology. Clarendon
Press, Oxford.
Rushchakov, V. (1981). Akusticheskie xarakteristiki soglasnyx nivxskogo
jazyka (avtoreferat). Ph.D.dissertation, Akademija Nauk CCCP,
Leningradskoe otdelenie instituta jazykoznanija.
Savel’eva,V. and C.Taksami. (1970). Nivxsko-russkij slovar. [Nivkh-Russian
dictionary] Sovetskaja Enciklopedija, Moskva.
Selkirk, E. (1986). On derived domains in sentence phonology . Phonology
Yearbook, 3: 371-405.
Shiraishi, H. (2000). Nivkh consonant alternation does not involve hardening.
Journal of Chiba University Eurasian Society. No.3. 89-119
(Also available at www.let.rug.nl/˜toshi/list_of_publication.htm).
Abridged version has appeared in the Proceedings of the 120 th
meeting of the Japanese Society of Linguists, 42-47.
Shternberg, L. (1908). Materialy po izucheniju gilijackogo jazyka i fol’klora.
In: Obrachy narodnoj slovesnosti. Vol. 1, Part I. Imper.
Akademii Nauk, St.Petersburg.
Steriade, D. (2000). Paradigm Uniformity and the Phonetics-Phonology
boundary. In: M. Broe and J. Pierrehumbert (eds.). Papers in
Laboratory Phonology 5. Cambridge University Press,
Cambridge, 313-334
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Linguistique de Prague, Prague.

The Influence of Speech Rate on Rhythm Patterns
Maartje Schreuder and Dicky Gilbers
1. Introduction 1
The topic of this paper is how rhythmic variability in speech can be
accounted for both phonologically and phonetically. The question is
whether a higher speech rate leads to adjustment of the phonological
structure, or just to 'phonetic compression', i.e. shortening and merging of
vowels and consonants, with preservation of the phonological structure. We
claim that the melodic content of a phonological domain is indeed adjusted
optionally when the speech rate increases. In other words, every speech rate
has its own preferred register, in terms of Optimality Theory (Prince and
Smolensky, 1993) its own ranking of constraints.
We will investigate prosodic variability as part of our main research
project, which involves a comparison of the analyses of music and
language. Our ultimate aim is to provide evidence for the assumption that
every temporal behavior is structured similarly (cf. Liberman, 1975).
Gilbers and Schreuder (to appear) show that Optimality Theory owes a lot
to the constraint-based music theory of Lerdahl and Jackendoff (1983).
Based on the great similarities between language and music we claim that
musical knowledge can help in solving linguistic issues.
In this paper, we will show that clashes are avoided in allegro tempo. In
both language and music distances between beats are enlarged, i.e. there
appears to be more melodic content between beats. To illustrate this, we ran
a pilot experiment in which we elicited fast speech. As expected, speech
rate plays a role in rhythmic variability.
The paper is organized as follows. In section 2 the data of the
experiment is introduced. Section 3 is addressed to the phonological
framework of Optimality Theory and the different rankings of andante and
allegro speech. The method of the experiment is discussed in section 4 and
the auditive and acoustic analyses plus the results follow in section 5. The
perspectives of our analysis will be discussed in the final section.

184 Maartje Schreuder and Dicky Gilbers
2. Data
We will discuss three types of rhythmic variability in Dutch. The first we
will call “stress shifts to the right”; the second “stress shifts to the left” and
the third “beat reduction”. In the first type as exemplified in stúdietòelage
(s w s w w) ‘study grant’, we assume that this compound can be realized as
stúdietoelàge (s w w s w) in allegro speech. Perfèctioníst (w s w s) is an
example of “stress shift to the left” and we expect a realization pèrfectioníst
(s w w s) in allegro speech. The last type does not concern a stress shift, but
a stress reduction. In zùidàfrikáans (s s w s) ‘South African’ compounding
of zuid and afrikaans results in a stress clash. In fast speech this clash is
avoided by means of reducing the second beat: zùidafrikáans (s w w s).
Table 1 shows a selection of our data.
Table 1. Data
Type 1: stress shift to the right (andante: s w s w w; allegro: s w w s w)
stu die toe la ge ‘study grant’
weg werp aan ste ker ‘disposable lighter’
ka mer voor zit ter ‘chairman of the House of Parliament’
Type 2: stress shift to the left (andante: w s w s; allegro: s w w s)
per fec tio nist ‘perfectionist’
a me ri kaan ‘American’
vi ri li teit ‘virility’
Type 3: beat reduction (andante: s s w s; allegro: s w w s)
zuid a fri kaans ‘South African’
schier mon nik oog ‘name of an island’
gre go ri aans ‘Gregorian’
The different rhythmic patterns are accounted for phonologically within the
framework of OT.
3. Framework and phonological analysis
The mechanism of constraint interaction, the essential characteristic of OT,
is also used in the generative theory of tonal music (Lerdahl and
Jackendoff, 1983). In both frameworks, constraint satisfaction determines
grammaticality and in both frameworks the constraints are potentially

The Influence of Speech Rate on Rhythm Patterns 185
conflicting and soft, which means violable. Violation, however, is only
allowed if it leads to satisfaction of a more important, higher ranked
constraint. The great similarities between these theoretical frameworks
make comparison and interdisciplinary research possible.
For example, restructuring rhythm patterns as a consequence of a higher
playing rate is a very common phenomenon in music. In Figure 1 we give
an example of re-/misinterpretation of rhythm in accelerated or sloppy
playing.
Dotted notes rhythm � triplet rhythm
Figure 1. Rhythmic restructuring in music
In Figure 1, the “dotted notes rhythm” (left of the arrow) is played as a
triplet rhythm (right of the arrow). In the dotted notes rhythm the second
note has a duration which is three times as long as the third, and in the
triplet rhythm the second note is twice as long as the third. In fast playing it
is easier to have equal durations between note onsets. Clashes are thus
avoided and one tries to distribute the notes, the melodic content, over the
measures as evenly as possible, even if this implies a restructuring of the
rhythmic pattern. To ensure that the beats do not come too close to each
other in fast playing, the distances are enlarged, thus avoiding a staccatolike
rhythm. In short, in fast tempos the musical equivalents of the
Obligatory Contour Principle (OCP), a prohibition on adjacency of
identical elements in language (McCarthy, 1986), become more important.
We claim that - just as in music - the allegro patterns in all the different
types of data in Table 1 are caused by clash avoidance. There is a
preference for beats that are more evenly distributed over the phrase. The
different structures can be described phonologically as a conflict between
markedness constraints, such as FOOT REPULSION (���) (Kager, 1994), and
OUTPUT - OUTPUT CORRESPONDENCE constraints (cf. Burzio, 1998) within
the framework of OT. FOOT REPULSION prohibits adjacent feet and
consequently prefers a structure in which feet are separated from each other
by an unparsed syllable. This constraint is in conflict with PARSE-σ, which

186 Maartje Schreuder and Dicky Gilbers
demands that every syllable is part of a foot. OUTPUT - OUTPUT
CORRESPONDENCE compares the structure of a phonological word with the
structure of its individual parts. For example, in a word such as fototoestel
'photo camera', OUTPUT - OUTPUT CORRESPONDENCE demands that the
rhythmic structure of its part tóestel 'camera' with a stressed first syllable is
reflected in the rhythmic structure of the output. In other words, OUTPUT -
OUTPUT CORRESPONDENCE prefers fótotòestel, with secondary stress on toe,
to fótotoestèl, with secondary stress on stel.
Whereas the normal patterns in andante speech satisfy OUTPUT -
OUTPUT CORRESPONDENCE, the preference for triplet patterns in fast speech
is accounted for by means of dominance of the markedness constraint,
FOOT REPULSION, as illustrated in Table 2. 2
Table 2. Rhythmic restructuring in language
a. ranking in andante speech:
constraints →
fototoestel
candidates ↓
OUTPUT - OUTPUT
CORRESPONDENCE
��� PARSE-σ
� (fóto)(tòestel) *
(fóto)toe(stèl) *! *
b. ranking in allegro speech:
constraints →
fototoestel
candidates ↓
(fóto)(tòestel) *!
OUTPUT - OUTPUT
���
CORRESPONDENCE
� (fóto)toe(stèl) * *
PARSE-σ
Dutch is described as a trochaic language (Neijt and Zonneveld, 1982).
Table 2a shows a preference for an alternating rhythm. The dactyl pattern
as preferred in Table 2b, however, is a very common rhythmic pattern of
prosodic words in languages such as Estonian and Cayuvava: every strong
syllable alternates with two weak syllables (cf. Kager, 1994). We assume
that the rhythm grammar, i.e. constraint ranking, of Dutch allegro speech

The Influence of Speech Rate on Rhythm Patterns 187
resembles the grammar of these languages. In the next section we will
explore whether we can find empirical evidence for our hypothesis.
4. Method
To find out whether people indeed prefer triplet patterns in allegro speech,
we ran a pilot experiment in which we tried to elicit fast speech. Six
subjects participated in a multiple-choice quiz in which they competed each
other in answering twenty simple questions as quickly as possible. In this
way, we expected them to speak fast without concentrating too much on
their own speech. In Table 3 one of the quiz items is depicted.
Table 3. Quiz item
Q4 President Bush is een typische ‘President Bush is a typical ’
A1 intellectueel ‘intellectual’
A2 amerikaan ‘American’
A3 taalkundige ‘linguist’
We categorized the obtained data as allegro speech. As a second task the
subjects were asked to read out the answers at a normal speaking rate
embedded in the sentence ik spreek nu het woord … uit 'now I pronounce
the word … '. This normal speaking rate generally means that the subjects
will produce the words at a rate of approximately 180 words per minute,
which we categorize as andante speech. All data were recorded on minidisk
in a soundproof studio and normalized in CoolEdit in order to improve the
signal-noise (S/N) ratio. Normalizing to 100% yields an S/N ratio
approaching 0 dB.
Six trained listeners judged the data auditively and indicated where they
perceived secondary stress. After this auditive analysis the data were
phonetically analyzed in PRAAT (Boersma and Weenink, 1992). We
compared the andante and allegro data by measuring duration, pitch,
intensity, spectral balance and rhythmic timing (Sluijter, 1995; Couper-
Kuhlen, 1993; Cummins & Port, 1998; Quené & Port, 2002; a.o.). Sluijter
claims that, respectively, duration and spectral balance are the main
correlates of primary stress. In our experiment, we are concerned with
secondary stress.

188 Maartje Schreuder and Dicky Gilbers
For the duration measurements, the rhymes of the relevant syllables
were observed. For example, in the allegro style answer A2 amerikaan in
Table 3, we measured the first two rhymes and compared the values in
Msec. with the values for the same rhymes at the andante rate. In order to
make this comparison valid, we equalized the total durations of both
realizations by multiplying the duration of the allegro with a so-called
'acceleration factor', i.e. the duration of the andante version divided by the
duration of the allegro version. According to Eefting and Rietveld (1989)
and Rietveld and Van Heuven (1997), the just noticeable difference for
duration is 4,5%. If the difference in duration between the andante and the
allegro realization did not exceed this threshold, we considered the
realizations as examples of the same speech rate and neglected them for
further analysis.
For the pitch measurements, we took the value in Hz in the middle of
the vowel. The just noticeable difference for pitch is 2,5% ('t Hart et al,
1990). For the intensity measurements, we registered the mean value in dB
of the whole syllable.
The next parameter we considered concerns spectral balance. Sluijter
(1995) claims that the spectral balance of the vowel of a stressed syllable is
characterized by more perceived loudness in the higher frequency region,
because of the changes in the source spectrum due to a more pulse-like
shape of the glottal waveform. The vocal effort, which is used for stress,
generates a strongly asymmetrical glottal pulse. As a result of the shortened
closing phase, there is an increase of intensity around the four formants in
the frequency region above 500 Hz. Following Sluijter (1995) we compared
the differences in intensity of the higher and lower frequencies of the
relevant syllables in both tempos.
Finally, we considered rhythmic timing. The idea is that the beats in
speech are separated from each other at an approximately equal distance
independent of the speech rate. In other words, a speaker more or less
follows an imaginary metronome. If he/she speaks faster, more melodic
content will be placed between beats, which results in a shift of secondary
stress. This hypothesis will be confirmed if the distance between the
stressed syllables in the andante realization of an item, e.g. stu and toe in
studietoelage, approximates the distance between the stressed syllables in
the allegro realization of the same item, e.g. stu and la. If the quotient of the
andante beat interval duration divided by the allegro beat interval duration
approximates 1, we expect perceived restructuring.

5. Results
5.1. Auditive analysis
The Influence of Speech Rate on Rhythm Patterns 189
Before we can present an auditive analysis of the data, we have to find out
whether or not the quiz design was successful. The results show that the
quiz indeed triggers faster speech by all subjects. Figure 2 shows their
acceleration factors. Subjects 1, 2 and 4 turned out to be the best
accelerating speakers, whereas subjects 3, 5 and 6 showed less difference in
duration between andante and allegro realizations. The mean acceleration
factor for the three fast speakers is 1.31, whereas the mean acceleration
factor for the three slow speakers is 1.13.
factor
1.4
1.35
1.3
1.25
1.2
1.15
1.1
1.05
1
acceleration factors
p1 p2 p3 p4 p5 p6
subjects
Figure 2. Acceleration factors of all subjects
Figure 3 shows the mean durations of the items at both speech rates. It
shows that the best accelerating speakers are also the fastest speakers. We
expect to find more restructured patterns for these speakers, mainly subjects
1 and 4, in comparison to the slower speakers, such as subjects 3 and 6.

190 Maartje Schreuder and Dicky Gilbers
seconds
1.15
1.05
0.95
0.85
0.75
0.65
mean word durations
p1 p2 p3 p4 p5 p6
subjects
Figure 3. Mean word durations
andante
Figure 4 shows that most subjects prefer patterns in which from a
phonological point of view markedness constraints dominate the
correspondence constraints at both rates for right and left shift data, but not
for beat reduction data. There are slightly more restructured patterns in
allegro tempo, although the differences are quite small.
Number
40
35
30
25
20
15
10
5
0
Right Shifts
(N=42)
Left Shifts
(N=36)
Types
All Subjects
Beat
Reductions
(N=42)
Figure 4. All subjects: Number of restructured items per type
When we take the results of two fast subjects apart, subjects 1 and 4, we
observe a stronger preference for restructuring in allegro speech and no
restructuring in andante speech, as shown in Figure 5. In other words, the
fast subjects display both a greater difference in word durations in andante
and allegro speech, and more variability in their speech patterns due to
tempo than the slow subjects do.
allegro
Not Shifted Andante
Shifted Andante
Shifted Allegro
Not Shifted Allegro

Number
16
14
12
10
8
6
4
2
0
Right Shifts
(N=14)
Fast Subjects
Left Shifts
(N=12)
Types
The Influence of Speech Rate on Rhythm Patterns 191
Beat
Reductions
(N=14)
Not Shifted Andante
Shifted Andante
Shifted Allegro
Not Shifted Allegro
Figure 5. Fast subjects: Number of restructured items per type
Obviously, the preference for restructuring the rhythmic pattern in allegro
speech is not an absolute preference. Sometimes restructuring does not take
place in allegro speech, but on the other hand restructured patterns also
show up in andante speech. 3 Some items were realized with the same
rhythmic pattern irrespective of the tempo. Therefore, we also looked at the
word pairs with a different rhythmic pattern in both tempos for each
subject. We observe that the relatively fast speakers p1, p2 and p4, show
the expected pattern according to our hypothesis, which means that they
show a restructured pattern in allegro tempo, as shown in Figure 6 for the
right shifts.
number
4
3
2
1
0
Right Shifts Word Pairs
p1 p2 p3 p4 p5 p6
subjects
Expected combinations
Counterexamples
Figure 6. Right Shifts: Expected combinations
Two of the relatively slow speakers, p3 and p6, show one counterexample
each, where the subject prefers the restructured patterns in andante tempo.
The other slow speaker, P5, displays no different patterns in andante and

192 Maartje Schreuder and Dicky Gilbers
allegro at all. Clearly, we have two different groups of speakers and this
observation strengthens our claim that restructuring relates to speech rate.
Some items, such as hobbywerkruimte (Type 1) 'hobby room', never
show a stress shift and other items, such as viriliteit (Type 2) ‘virility’,
prefer the shifted pattern in both tempos for all subjects. Possibly, the
syllable structure plays an important role; open syllables seem to lose stress
more easily than closed ones.
5.2. Acoustic analysis
In the current state of phonological research, embodied in e.g. laboratory
phonology, much value is set on acoustic evidence for phonological
analyses. Studies such as Sluijter (1995) and Sluijter and Van Heuven
(1996) provide acoustic correlates for primary stress. In our study we are
concerned with beat reduction and secondary stress shifts and we wonder
whether or not the same acoustic correlates hold for secondary stress.
Shattuck Hufnagel et al (1994) and Cooper and Eady (1986) do not find
acoustic correlates of rhythmic stress at all. They claim that it is not entirely
clear which acoustic correlates are appropriate to measure, since these
correlates are dependent on the relative strength of the syllables of an
utterance. The absolute values of a single syllable can hardly be compared
without reference to their context and the intonation pattern of the complete
phrase. Huss (1978) claims that some cases of perceived rhythmic stress
shift may be perceptual rather than acoustic in nature. Grabe and Warren
(1995) also suggest that stress shifts can only be perceived in rhythmic
contexts. In isolation, the prominence patterns are unlikely to be judged
reliably. In the remainder of this paper we try to find out if we can support
one of these lines of reasoning. In other words, are we able to support our
perceived rhythmic variability with a phonetic analysis? Therefore, we
measured the duration, pitch, intensity, spectral balance and rhythmic
timing of the relevant syllables as realized by subject P1.
Because Dutch is a quantity-sensitive language, the duration of the
relevant syllable rhymes was considered. Onsets do not contribute to the
weight of a syllable. In Figure 7, the duration analysis is shown for Type 2
data (left shifts). The four columns indicate, respectively, the duration of
the rhyme of the first and second syllable in andante speech, and the
duration of the first and second one in allegro speech. According to Sluijter
(1995), duration is the main correlate of primary stress. As a starting point,

The Influence of Speech Rate on Rhythm Patterns 193
we adopt her claim for our analysis of secondary stress. Our measurements
would confirm our hypothesis and our auditive analysis, if the second
column were higher than the first one and if the fourth column were lower
than the third one. In that case, the subject would realize a word such as
perfectionist as perfèctioníst in andante tempo and as pèrfectioníst in
allegro tempo.
In the andante tempo, three out of six items show the dominant
correspondence pattern and in the allegro tempo, four out of six items show
the dominant markedness pattern. That is hardly a preference and it does
not confirm our auditive analysis of the same data. Furthermore, if we
consider the word pairs with different patterns, there is only one pair that
has the ideal ratio: the patterns of amerikaan.
seconds
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
perfectionist
amerikaan
duration rhymes
piraterij
verbaliseren
item
banaliteit
Figure 7. Duration (Left Shifts by Subject P1)
If duration does not enable us to confirm our auditive findings, maybe pitch
is the main stress correlate for this speaker. However, pitch measurements
reveal the same fuzzy result as the duration measurements. Again, only one
pattern confirms the auditive analysis. This time it is not the item
amerikaan, but the item perfectionist. Moreover, the differences in pitch in
this item do not exceed the threshold of the 2.5%, which is the just
noticeable difference for pitch. We also analyzed the mean intensity value
of the relevant vowels without recognizable patterns between allegro and
andante style. These results support the analyses of Sluijter (1995) and
Sluijter and Van Heuven (1996), who also claim that the intensity
parameter does not contribute much to the perception of stress.
Next, we considered the spectral balance. In order to rule out the
influence of the other parameters, we monotonized the data for volume and
viriliteit
andante r1
andante r2
allegro r1
allegro r2

194 Maartje Schreuder and Dicky Gilbers
pitch. Then we selected the relevant vowels and analyzed them as a
cochleagram in PRAAT. The cochleagram simulates the way the tympanic
membrane functions, in other words the way in which we perceive sounds.
In Figure 8 we show two cochleagrams of the vowel [a] in the fourth
syllable of, respectively, stúdietòelage 'study grant' (Type 1) in andante
tempo and stúdietoelàge in allegro tempo. This item was taken from a prestudy.
The allegro data show the expected increased perceived loudness in
the higher frequencies, indicated by means of shades of gray; the darker
gray the more perceived loudness.
25
20
15
10
5
0
0 0.169371
Time (s)
Figure 8. Cochleagrams of [�] in studietoelage
0
0 0.143209
Time (s)
The right cochleagram (stressed [a]) in Figure 8 shows increased perceived
loudness in the regions of approximately 5 to 22 Bark in the allegro version
of [a] in comparison with the left cochleagram (unstressed [a]). This
confirms the results of the study of primary stress in Sluijter (1995). If we
convert this perceptive, almost logarithmic, Bark scale into its linear
counterpart, the Hertz scale, this area correlates with the frequency region
of 3 to 10 kHz.
In order to measure perceived secondary stress, we will measure the
relative loudness in the different frequency regions in Phon. 4 According to
Sluijter (1995) stressed vowels have increased loudness above 500 Hz
compared to the same vowel in an unstressed position. This can be shown if
we take a point in time from both cochleagrams in Figure 8 in which the F1
reaches its highest value (following Sluijter, 1995). In Figure 9 the values
in Phon are depicted for these points and plotted against the Bark values in
25 steps.
25
20
15
10
5

phon
60
50
40
30
20
1
stressed and unstressed [a]
5
9
13
bark
Figure 9. Loudness in Phon
17
The Influence of Speech Rate on Rhythm Patterns 195
21
25
andante
a lle g ro
The white line in Figure 9 indicates the pattern of the allegro stressed [a] in
studietoelage and the black line indicates the pattern of the andante
unstressed [a]. We see increased loudness in the region of 13 to 21 Bark,
which correlates with the most sensitive region of our ear. The mean Phon
value in Figure 9 between 5 and 21 Bark is 43.6 Phon for the andante
unstressed [a] and 47.4 Phon for the allegro stressed [a]; a mean difference
of 3.8 Phon.
Now, let us see whether or not we can find similar results for our subject
P1. Figure 10 shows that the spectral balance confirms the leftward stress
shift we perceived in the allegro realization of amerikaan. The first syllable
vowel in allegro tempo is characterized by more loudness in the higher
frequency regions than its andante counterpart. In the second syllable vowel
it is just the other way around.
Phon
50
40
30
20
10
0
Stressed and unstressed [a] in
[a]merikaan
1
4
7
10
13
16
19
22
25
Bark
andante
allegro
Stressed and unstressed [e] in
a[me]rikaan
Figure 10. Spectral balance comparison of the first two vowels of amerikaan
Unfortunately, not all spectral balance data confirm our auditive analysis.
For example, we claimed that the pitch analysis of the stress shift in
Phon
60
40
20
0
1
4
7
10
13
16
19
22
25
Bark
andante
allegro

196 Maartje Schreuder and Dicky Gilbers
perfectionist did confirm our auditive analysis. Therefore, we expected
more loudness in the allegro realization of the first vowel and less loudness
in the allegro realization of the second vowel, but it appeared that there is
relatively more loudness in the andante realization of per. This result
contradicts our auditive and our pitch analysis.
We have to conclude that the different phonetic analyses contradict each
other. Sometimes the perceived stress shift is characterized by a longer
duration of the stressed syllable; sometimes a relatively higher pitch
characterizes it. The results of our spectral balance analysis show that the
differences in loudness pattern with differences in duration. In our
perceived stress shift in allegro perfectionist, pitch turned out to be the
decisive correlate, whereas duration and spectral balance measurements
indicated no shift at all. On the other hand, the perceived shift in allegro
amerikaan was confirmed by the duration and spectral balance analyses
together, whereas pitch measurements indicated the opposite pattern. For
most perceived stress shifts, however, the acoustic correlates did not give
any clue.
Finally, we will consider whether the perception of restructuring
depends on rhythmic timing. Just as in music, speech can be divided into a
melodic string and a rhythmic string as partly independent entities. With
respect to speech, the melodic string seems to be more flexible than the
rhythmic one. Imagine that the rhythm constitutes a kind of metronome
pulse to which the melodic content has to be aligned. The listener expects
prominent syllables to occur with beats. This behavior is formulated as the
Equal Spacing Constraint: prominent vowel onsets are attracted to
periodically spaced temporal locations (Couper-Kuhlen, 1993; Cummins &
Port, 1998; Quené & Port, 2002; a.o.). Dependent on speech rate the
number of intervening syllables between beats may differ. Suppose the beat
interval is constant at 300 msec., there will be more linguistic material in
between in allegro speech, e.g. the two syllables die and toe in
stúdietoelàge, than in andante speech, e.g. only one syllable die in
stúdietòelage.
If indeed the perception of secondary stress shifts depends on rhythmic
timing, i.e. the beat interval between prominent syllables in andante and
allegro speech is approximately equal, than we expect that the duration
quotient of the interval between, for example, stu and toe in the andante
realization of studietoelage and stu and la in the allegro realization
approximates 1.

The Influence of Speech Rate on Rhythm Patterns 197
In our pre-study, the interval between the vowel onsets of the first and
third syllable in studietoelage (andante) is 0.358 sec, whereas the interval
between the first and the fourth syllable in the allegro realization of the
same word is 0.328 sec. This means that the duration quotient is 1.091,
which indeed approximates 1. In other words, this example supports the
idea of the Equal Spacing Constraint.
Does the same result hold for our present data? We measured the beat
intervals between all possible stress placement sites for all six subjects.
Figure 11 depicts the duration quotients for subject 1. Figure 12 shows the
beat intervals of the same data. It depicts as well the duration interval
between the first and the third, as the first and fourth syllable for both
speech rates. We expect restructuring for those data in which the line of the
first to third syllable interval (andante (black line)) coincides with the line
of the first to fourth syllable interval (allegro (white line)).
quotient
1.1
1
0.9
0.8
0.7
0.6
0.5
studietoelage
Right Shifts beat intervals andante:allegro
wegwerpaanst...
trimesterindeling
kamervoorzitter
hobbywerkruimte
gemeenteinschr...
Figure 11. Quotient beat intervals of Subject P1
beat intervals in sec
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
studietoelage
wegwerpaansteker
trimesterindeling
Figure 12. Beat intervals of Subject P1
winkelopheffing
Right Shifts Beat Intervals P1
kamervoorzitter
hobbywerkruimte
gemeenteinschrijving
winkelopheffing
quotient
andante s1-3
andante s1-4
allegro s1-4
allegro s1-3

198 Maartje Schreuder and Dicky Gilbers
The Figures 11 and 12 indicate that the relevant beat intervals of the items
1, 4 and 7, studietoelage 'study grant', kamervoorzitter 'chairman of the
House of Parliament' and winkelopheffing 'closing down of a shop',
respectively, coincide. In other words, we expect to hear restructuring in
exactly these three items.
Unfortunately, our auditive analysis indicates only attested
combinations of restructuring in items 2 and 6: wegwerpaansteker
'disposable lighter' and gemeente-inschrijving 'municipal registration',
respectively. Obviously, rhythmic timing is not the decisive characteristic
of perceived restructuring in allegro speech either.
6. Discussion and Conclusion
In section 4, we presented our phonological account of the restructuring
within the framework of OT. Our main conclusion is that phonetic
compression cannot be the sole explanation of the different rhythm
patterns. Although the results cannot really confirm our hypothesis that
there are different grammars, i.e. constraint rankings for different rates of
speaking, there seems to be something that relates to speech rate. The fast
speakers display different grammars, i.e. constraint rankings, for different
rates of speaking. In their andante tempo, correspondence constraints
prevail, whereas in allegro tempo markedness constraints dominate the
correspondence ones. These preferences resemble the preferences of
andante and allegro music. In both disciplines clashes are avoided in
allegro tempo by means of enlarging the distances between beats.
In section 5, we attempted to confirm our phonological account with a
phonetic analysis. Unfortunately, the phonetic correlates of stress -
duration, pitch, intensity and spectral balance - do not show the expected
and perceived differences in rhythm patterns in all pairs. Sluijter (1995)
found out that duration is the main correlate of primary stress with spectral
balance as an important second characteristic. In our analysis, however,
neither differences in duration nor differences in spectral balance could
identify secondary stress. Therefore, we have to conclude that our analysis
supports earlier work by Shattuck Hufnagel et al (1994), Cooper and Eady
(1986), Huss (1978) and Grabe and Warren (1995), who all claim that
acoustic evidence for secondary stress cannot be found unambiguously.
Although we did find some differences in duration, spectral balance or
pitch, these differences were not systematically found in all pairs in which

The Influence of Speech Rate on Rhythm Patterns 199
we perceived rhythmic variability. Finally, we discussed rhythmic timing as
a cue for variable patterns. However, the hypothesis that the duration
between prominent syllables is approximately equal in both andante and
allegro speech was not confirmed by the auditive analysis of the data. It
seems that rhythmic restructuring is more a matter of perception than of
production. At this point, the question remains: are we fooled by our brains
and is there no phonetic correlate of the perceived phonological stress shifts
in the acoustic signal or do we have to conclude that the real phonetic
correlate of secondary stress has yet to be found?
Notes
1 This paper is an extension of our paper "Restructuring the melodic content of
feet", which is submitted to the proceedings of the 9 th International Phonology
Meeting: Structure and melody, Vienna 2002. We wish to thank Grzegorz
Dogil, Hidetoshi Shiraishi plus the participants of the 9 th International
Phonology Meeting, Vienna 2002 and the participants of the 11 th Manchester
Phonology Meeting, Manchester 2003 for their useful comments. We are also
grateful to Sible Andringa, Nynke van den Bergh, Gerlof Bouma, John Hoeks,
Jack Hoeksema, Wander Lowie, Dirk-Bart den Ouden, Joanneke Prenger,
Ingeborg Prinsen, Femke Wester for participating in our experiment. We
especially thank Wilbert Heeringa and Hugo Quené for supplying us with the
PRAAT scripts that we could use for our spectral balance and rhythmic timing
analyses.
2 For reasons of clarity, we abstract from constraints such as FOOTBINARITY
(FTBIN) and WEIGHT-TO-STRESS PRINCIPLE in Table 2. Although these
constraints play an important role in the Dutch stress system (cf. Gilbers &
Jansen, 1996), the conflict between OUTPUT-OUTPUT CORRESPONDENCE and
FOOT REPULSION is essential for our present analysis.
3 With respect to the phonological analysis of the data, we suggest a random
ranking of weighed correspondence and markedness constraints. By means of
weighing constraints we adopt an OT variant that more or less resembles the
analyses in OT’s predecessor Harmonic Grammar (cf. Legendre, G., Y. Miyata &
P. Smolensky, 1990). Note that we do not opt for a co-phonology for allegro-style
speech in our analysis. In a co-phonology, the output of the andante-style ranking
is input or base for the allegro-style ranking. We opt for a random ranking with
different preferences for allegro and andante speech, because our data show
variable rhythmic structures at both rates. Both rankings evaluate the same input
form.

200 Maartje Schreuder and Dicky Gilbers
4 The perceived loudness depends on the frequency of the tone. The Phon entity
is defined using the 1kHz tone and the decibel scale. A pure sinus tone at any
frequency with 100 Phon is as loud as a pure tone with 100 dB at 1kHz
(Rietveld and Van Heuven, 1997: 199). We are most sensitive to frequencies
around 3kHz. The hearing threshold rapidly rises around the lower and upper
frequency limits, which are respectively about 20Hz and 16kHz.
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List of Addresses
Drs. Markus Bergmann
University of Groningen, Faculty of Arts, Department of Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635982, M.Bergmann@let.rug.nl
Drs. Tamás Bíró
University of Groningen, Faculty of Arts, Department of Computational
Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3636852, Birot@let.rug.nl
Dr. Dicky Gilbers
University of Groningen, Faculty of Arts, Department of Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635983, D.G.Gilbers@let.rug.nl
Dr. Charlotte Gooskens
University of Groningen, Faculty of Arts, Department of Scandinavian
Languages and Cultures
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635827, C.S.Gooskens@let.rug.nl
Dr. Dr. Tjeerd de Graaf and Drs. Nynke de Graaf
University of Groningen, Faculty of Arts, Department of Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635982, T.de.Graaf@let.rug.nl
Drs. Angela Grimm
University of Groningen, Faculty of Arts, Department of Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635920, A.Grimm@let.rug.nl
Dr. Ing. Wilbert Heeringa
University of Groningen, Faculty of Arts, Department of Computational
Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635970, W.J.Heeringa@let.rug.nl

204
Prof. Dr. Vincent J. van Heuven
University of Leiden, Faculty of Arts, Department of Linguistics
Van Wijkplaats 4, 2311 BX Leiden, The Netherlands
+31 71 5272105, V.J.J.P.van.Heuven@let.leidenuniv.nl
Nienke Knevel
p/a University of Groningen, Faculty of Arts, Department of Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635983, N.B.Knevel@student.rug.nl
Dr. Jurjen van der Kooi
University of Groningen, Faculty of Arts, Department of Frisian
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635966, J.van.der.Kooi@let.rug.nl
Prof. Dr. Ir. John Nerbonne
University of Groningen, Faculty of Arts, Department of Computational
Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635815, J.Nerbonne@let.rug.nl
Drs. Maartje Schreuder
University of Groningen, Faculty of Arts, Department of Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635920, M.J.Schreuder@let.rug.nl
Drs. Hidetoshi Shiraishi
University of Groningen, Faculty of Arts, Department of Linguistics
Oude Kijk in 't Jatstraat 26, 9712 EK Groningen, The Netherlands
+31 50 3635982, H.Shiraishi@let.rug.nl
Dr. Ivilin Stoianov
University of Padova, Department of General Psychology
Via Venezia 8, 35100 AS Padova, Italy
+39 049 8276676, Ivilin.Stoianov@unipd.it