Prosody and Syntax in Corpus Based Analysis of Spoken English ...

Prosody and Syntax in
Corpus Based Analysis of
Spoken English
by
Simon Christopher Arneld
The University of Leeds
School of Computer Studies
December 14, 1994
Submitted in accordance with the requirements
for the degree of Doctor of Philosophy.
The candidate conrms that the work submitted is his own and that
appropriate credit has been given where reference has been made to
the work of others.

Abstract
This thesis attempts to show that it can be productive to analyse English prosody in terms of
syntax. Although diering prosodies are possible for a xed syntax, it is demonstrated that an
utterances syntax can be used to generate an underlying \baseline" prosody regardless of the
actual words, semantics or context. In order to analyse this a British English spoken corpus is
needed which has both syntactic and prosodic information. Such a corpus (the Spoken English
Corpus (SEC) now known as the Machine Readable Spoken English Corpus (MARSEC)) is used
to calculate a number of statistical measures relating the prosodic (specically the tonic stress
mark annotations) and the syntactic (specically the part of speech tags) information.
This thesis explores the mapping between this information. Models are devised around this
information which implement the mappings and select from the search space of possible annotations
those with the highest scores. The mapping is applied in the models for prediction of stress
and prosodic annotations in new (part of speech tagged) text.
The models are used to demonstrate that there is a clear relationship between parts of speech
and the prosodic annotations in the Spoken English Corpus. The models may be exploited to
generate stress and prosodic annotations for text{to{speech applications in order to increase the
intelligibility and naturalness of the synthesized speech.

Contents
1 Introduction 1
1.1 Introduction :::::::::::::::::::::::::::::::::::::::: 1
1.2 Motivation :::::::::::::::::::::::::::::::::::::::: 2
1.3 Applications :::::::::::::::::::::::::::::::::::::::: 2
1.3.1 Speech Synthesis ::::::::::::::::::::::::::::::::: 2
1.3.2 Speech Recognition and Understanding :::::::::::::::::::: 3
1.4 Overview ::::::::::::::::::::::::::::::::::::::::: 4
2 Background 6
2.1 Introduction :::::::::::::::::::::::::::::::::::::::: 6
2.2 Prosody :::::::::::::::::::::::::::::::::::::::::: 6
2.2.1 Denition of prosody ::::::::::::::::::::::::::::::: 7
2.2.2 Auditory versus Acoustic Prosody ::::::::::::::::::::::: 12
2.2.3 Functions of prosody ::::::::::::::::::::::::::::::: 12
2.2.4 Representation of prosody :::::::::::::::::::::::::::: 13
2.3 Syntax ::::::::::::::::::::::::::::::::::::::::::: 14
2.3.1 Denition of Syntax ::::::::::::::::::::::::::::::: 14
2.4 Approaches To Natural Language Processing ::::::::::::::::::::: 16
2.5 Spoken English Corpora and Prosodic Annotation :::::::::::::::::: 17
2.5.1 London{Lund Corpus :::::::::::::::::::::::::::::: 18
i

2.5.2 Polytechnic of Wales Corpus :::::::::::::::::::::::::: 19
2.5.3 SEC/MARSEC :::::::::::::::::::::::::::::::::: 19
2.6 Computational Use of Prosody ::::::::::::::::::::::::::::: 20
2.6.1 Speech Recognition and Understanding :::::::::::::::::::: 22
2.6.2 Speech Synthesis ::::::::::::::::::::::::::::::::: 22
3 Relating Prosody and Word Class 24
3.1 Introduction :::::::::::::::::::::::::::::::::::::::: 24
3.2 A Source of Data ::::::::::::::::::::::::::::::::::::: 24
3.3 A Need For Processing :::::::::::::::::::::::::::::::::: 25
3.4 Cross Referencing ::::::::::::::::::::::::::::::::::::: 27
3.5 By{Products ::::::::::::::::::::::::::::::::::::::: 31
3.6 Summary ::::::::::::::::::::::::::::::::::::::::: 31
4 Preliminary Statistical Analysis 34
4.1 Introduction :::::::::::::::::::::::::::::::::::::::: 34
4.2 Prosodic Annotation Statistics ::::::::::::::::::::::::::::: 35
4.2.1 Prosodic mark frequencies :::::::::::::::::::::::::::: 35
4.2.2 Tone Unit lengths :::::::::::::::::::::::::::::::: 36
4.2.3 Prosodic mark bigram frequencies ::::::::::::::::::::::: 37
4.3 Cross{Reference Statistics :::::::::::::::::::::::::::::::: 38
4.3.1 Co{occurence tables ::::::::::::::::::::::::::::::: 38
4.3.2 Ignoring Higher{Level Syntactic structures :::::::::::::::::: 40
4.3.3 Clustering word classes ::::::::::::::::::::::::::::: 41
4.4 Summary ::::::::::::::::::::::::::::::::::::::::: 41
5 Automatic Stress Annotation 45
5.1 Introduction :::::::::::::::::::::::::::::::::::::::: 45
5.2 Stress Prediction ::::::::::::::::::::::::::::::::::::: 47
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5.2.1 Search Mechanism :::::::::::::::::::::::::::::::: 49
5.2.2 Scoring :::::::::::::::::::::::::::::::::::::: 49
5.2.3 Performance Measures :::::::::::::::::::::::::::::: 51
5.2.4 Context :::::::::::::::::::::::::::::::::::::: 52
5.2.5 Boundary Conditions :::::::::::::::::::::::::::::: 55
5.3 Performance :::::::::::::::::::::::::::::::::::::::: 56
5.4 Improvements ::::::::::::::::::::::::::::::::::::::: 58
5.5 Summary ::::::::::::::::::::::::::::::::::::::::: 61
6 Automatic Prosodic Annotation 63
6.1 Introduction :::::::::::::::::::::::::::::::::::::::: 63
6.2 Expanding the Model :::::::::::::::::::::::::::::::::: 63
6.3 Model Design ::::::::::::::::::::::::::::::::::::::: 65
6.3.1 Choice of Prosodic Marks :::::::::::::::::::::::::::: 66
6.3.2 Estimation of Probabilities ::::::::::::::::::::::::::: 67
6.3.3 The Model :::::::::::::::::::::::::::::::::::: 69
6.3.4 Composite Model ::::::::::::::::::::::::::::::::: 70
6.4 Model Assessment :::::::::::::::::::::::::::::::::::: 73
6.4.1 Performance Statistics :::::::::::::::::::::::::::::: 76
6.5 Summary ::::::::::::::::::::::::::::::::::::::::: 77
7 Conclusions and Future Work 78
7.1 Introduction :::::::::::::::::::::::::::::::::::::::: 78
7.2 Review ::::::::::::::::::::::::::::::::::::::::::: 78
7.3 Performance Measures :::::::::::::::::::::::::::::::::: 79
7.3.1 Tone Unit lengths in the Model. :::::::::::::::::::::::: 80
7.3.2 Analysis of Models :::::::::::::::::::::::::::::::: 80
7.3.3 Word Class Models :::::::::::::::::::::::::::::::: 82
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7.3.4 Prosodic Mark Models :::::::::::::::::::::::::::::: 83
7.4 Future Work ::::::::::::::::::::::::::::::::::::::: 84
7.4.1 Conversion to ToBI ::::::::::::::::::::::::::::::: 85
7.4.2 Additional Constraints :::::::::::::::::::::::::::::: 85
7.4.3 Speech Synthesis ::::::::::::::::::::::::::::::::: 86
7.4.4 Parameter Improvement ::::::::::::::::::::::::::::: 87
7.5 General Conclusions ::::::::::::::::::::::::::::::::::: 87
A SEC and MARSEC 88
A.1 Introduction :::::::::::::::::::::::::::::::::::::::: 88
A.2 The Spoken English Corpus ::::::::::::::::::::::::::::::: 88
A.2.1 History :::::::::::::::::::::::::::::::::::::: 88
A.2.2 Categories ::::::::::::::::::::::::::::::::::::: 88
A.3 MARSEC ::::::::::::::::::::::::::::::::::::::::: 89
B Syntactic Tagging of SEC 92
B.1 Introduction :::::::::::::::::::::::::::::::::::::::: 92
B.2 Word Class Tags ::::::::::::::::::::::::::::::::::::: 92
B.3 Phrase/Clause Tags ::::::::::::::::::::::::::::::::::: 96
C Testing Data 97
C.1 Corpus Texts: Category M ::::::::::::::::::::::::::::::: 97
C.1.1 Section M02 :::::::::::::::::::::::::::::::::::: 97
C.1.2 Section M03 :::::::::::::::::::::::::::::::::::: 98
C.1.3 Section M04 :::::::::::::::::::::::::::::::::::: 99
C.1.4 Section M05 :::::::::::::::::::::::::::::::::::: 100
C.1.5 Section M07 :::::::::::::::::::::::::::::::::::: 103
C.1.6 Section M08 :::::::::::::::::::::::::::::::::::: 103
C.1.7 Section M09 :::::::::::::::::::::::::::::::::::: 104
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C.2 Prediction Results :::::::::::::::::::::::::::::::::::: 106
C.2.1 Extract from section M05 :::::::::::::::::::::::::::: 106
D Word{Class / TSM Co{occurence gures 108
D.1 Tonic Stress Mark Frequencies. ::::::::::::::::::::::::::::: 108
D.2 Word Class Frequencies. ::::::::::::::::::::::::::::::::: 109
D.3 Tag/Tone Co-occurences ::::::::::::::::::::::::::::::::: 109
E Punctuation and Boundaries 114
F Source Code 116
F.1 symbolify.c :::::::::::::::::::::::::::::::::::::::: 116
F.2 ttalign.c :::::::::::::::::::::::::::::::::::::::::: 120
F.3 collate-tu.c :::::::::::::::::::::::::::::::::::::::: 128
F.4 align-parse.c :::::::::::::::::::::::::::::::::::::::: 130
F.5 splittule.c :::::::::::::::::::::::::::::::::::::::: 133
F.6 transition.c :::::::::::::::::::::::::::::::::::::::: 134
F.7 transgroups.c ::::::::::::::::::::::::::::::::::::::: 137
F.8 segment.c ::::::::::::::::::::::::::::::::::::::::: 141
F.9 probability.c :::::::::::::::::::::::::::::::::::::::: 142
F.10 probability3.c ::::::::::::::::::::::::::::::::::::::: 149
F.11 probabilityc.c ::::::::::::::::::::::::::::::::::::::: 156
Bibliography 171
v

List of Tables
2.1 Prosodic marks used in the SEC/MARSEC :::::::::::::::::::::: 21
4.1 Categories of the corpus used for analysis ::::::::::::::::::::::: 35
4.2 Prosodic mark bigram frequencies ::::::::::::::::::::::::::: 37
4.3 Co{occurence table for 64 most frequent word classes. :::::::::::::::: 43
5.1 Stress Transition Table. ::::::::::::::::::::::::::::::::: 54
5.2 Probability of a tone unit boundary following a stressed or unstressed word. :::: 56
5.3 Probability of stressed or unstressed word following a Tone Unit boundary. :::: 56
5.4 Performance statistics for stress prediction model. Percentage of words which are
correctly stressed/unstressed in comparison to the two expert annotations and overall. 57
5.5 Performance statistics for stress prediction model. Percentage of completely correct
tone units in comparison to the two expert annotations (BJW: Briony Williams,
and GOK: Gerry Knowles) and overall (ALL). :::::::::::::::::::: 57
5.6 Words classes in the groups. ::::::::::::::::::::::::::::::: 60
5.7 Performance statistics for stress prediction model using group transition probabilities. 61
6.1 Performance scores for the training categories. ::::::::::::::::::::: 72
6.2 Scoring relationship between predicted and annotated prosodic marks. ::::::: 75
6.3 Performance scores for the test category of the corpus. :::::::::::::::: 77
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7.1 Word class tags with frequencies of 50 or greater showing percentage of correct
predictions (when compared with the corpus annotations) for the stress prediction
model (SPM) and the prosodic mark prediction model (PPM). ::::::::::: 81
7.2 Prosodic marks showing prediction percentages for the composite prosody prediction
model. :::::::::::::::::::::::::::::::::::::::: 83
A.1 Categories in the SEC/MARSEC :::::::::::::::::::::::::::: 90
A.2 Sections in the SEC/MARSEC ::::::::::::::::::::::::::::: 90
B.1 Phrase and Clause labels ::::::::::::::::::::::::::::::::: 96
E.1 Punctuation/Tone Unit Boundary Co-occurence Table. :::::::::::::::: 115
vii

List of Figures
2.1 Waveform for the vowel e with one cycle or pitch period marked. :::::::::: 9
3.1 Example of Prosodic annotation format. :::::::::::::::::::::::: 32
3.2 Example of Treebank format. :::::::::::::::::::::::::::::: 32
3.3 Example output from cross{referencing from section B02. : : : : : : : : : : : : : : 33
4.1 Frequency of prosodic marks :::::::::::::::::::::::::::::: 36
4.2 Relative frequencies of tone{unit lengths in terms of numbers of: words with tonic
stress marks words with prosodic marks and words. ::::::::::::::::: 36
4.3 Hierarchical clustering of 64 most frequent word classes. ::::::::::::::: 44
7.1 Relative frequencies of tone-unit lengths produced by the model in terms of numbers
of: words with tonic stress marks words with prosodic marks and words. ::::: 82
A.1 Diagram showing waveform, fundamental frequency, RMS energy, segmental, prosodic
and treebank transcriptions. ::::::::::::::::::::::::::::::: 91
viii

Acknowledgements
Iwould like to thank my supervisors Eric Atwell and Peter Roach for their support, advice, wisdom
and ideas. Gratitude must also go to my collegues in the Speech Laboratory and in the School of
Computer Studies who helped me with a great many things.
Iwould also like to thank my family and friends especially my wife Debra and my friend Dean
Brown who have put up with my rantings over the last few years and gave me their condence
and enthusiasm to continue when things looked bleak.
This research was funded by a Science and Engineering Research Council (SERC) research
studentship.
ix

Chapter 1
Introduction
1.1 Introduction
This thesis investigates the relationship between parts of speech (or word class) and prosodic annotations
specically in the Spoken English Corpus. A probabilistic approach istaken to describe
the mapping from word class annotations to prosodic annotations. This mapping quanties the
relationship.
It is shown that a strong relationship exists between word class tags and stress (see chapter 5)
but to a lesser extent there is also a relationship with prosodic annotations (see chapter 6). Models
developed to demonstrate the relationship achieve over 91% agreement with the original corpus
annotations for stress prediction and over 65% agreement for prosody (stress accent) prediction.
The principal aim in studying the relationship is to assess whether either can be of any use in
determining the other in speech synthesis, speech recognition and speech understanding applications.
1

1.2 Motivation
Lea[Lea80] (pp.172{174) gives results of an experiment where ve listeners were presented with
255 carefully designed spoken sentences. The listeners were asked to mark each syllable as either
stressed, unstressed or reduced. Lea collated their results and presented them as relative stress
level ordered according to syntactic category (see his gure 8{2 p.174 in[Lea80]).
His experiment showed that articles, conjunctions, and prepositions were on average judged
reduced.
Possessive determiners, relatives, copulatives, auxiliary verbs and pronouns were on
average unstressed and main verbs, adjectives, sentence adverbs, nouns, quantiers and command
verbs were on average stressed.
This evidence shows that there is a relationship between stress and word class. Within this
thesis similar data is analysed but on a much larger scale since there are over 1400 sentences and
the stress distinctions extend to cover a wide variety of stress accents. Such ataskwould not be
possible without the use of a machine readable corpus such as the Spoken English Corpus the like
of which has not been available until recent years.
The results presented in chapters 4, 5, 6 and 7 conrm and expand upon Lea's results.
1.3 Applications
Information relating parts of speech to prosody has useful applications for speech recognition and
understanding and speech synthesis.
1.3.1 Speech Synthesis
In speech synthesis we have a one{to{many relationship between the string of word class tags
for the words in an utterance and all the possible prosodic patterns that can be used with that
utterance.
It is apparent that context will aect the choice of prosodic patterns beyond the scope derivable
from word class information. Consider the utterance \Peter isn't here". The tonic (or main) stress
2

accent may be placed on any word to eect dierent emphasis and attitudinal circumstances. Viz:
1. Peter isn't here.
2. Peter isn't here.
3. Peter isn't here.
Where the small capitals indicate the word taking the stress accent. The rst might be uttered
if everyone except Peter were present and if he were expected to be. The second might be uttered
to correct a persons misconception about the presence of Peter.
The nal utterance might be
made if we expected Peter to be somewhere when he is not.
It will be realised that the possible variations in prosody have multiple purposes covering
aspects of attitude, emphasis, given/new information and grammatical structure.
In this thesis we are only concerned with the information contained within the parts of speech
and how this relates to prosody and hence only the latter of the above aspects will have a major
bearing on this work. However, there is a certain amount of other information implicit within the
structure of the parts of speech.
Without the support of contextual information to distinguish between the above choices the
best that can be aimed for in speech synthesis is a discourse neutral pattern | or a\baseline" pattern
which would be the standard or default pronunciation in the absence of any major contextual
eects.
So, the main application of relating prosody and word class tags for text{to{speech speech
synthesis is in producing neutral prosodic patterns which, of course, may be modied by higher
level context or semantic processes. It is beyond the scope of this thesis to cover the realisation
of these prosodic patterns acoustically.
1.3.2 Speech Recognition and Understanding
In speech recognition the uses of prosody are more limited because identifying prosodic information
in an utterance will not help signicantly with the low{level identication of words or the words
3

classes. However, disambiguation between similar sounding words based upon pitch accents or
stress is a realistic possibility. For example \which" and \witch" sound the same but will most
likely have dierent prosody. The word \which" (word class tag DDQ | `wh-' determiner without
`-ever') is most likely to be unstressed though it may be stressed approximately 1/3 of the time.
The word \witch" (word class tag NN1 | singular common noun) is highly likely to be stressed
with a variety of stress accents. Thus by determining the presence of stress we can postulate that
\witch" is more likely and by combining this with evidence from surrounding words for probable
syntactic structures we can remove the ambiguity. The data presented in chapter 4 and appendix D
is most useful for this task.
The most useful application of prosody is likely to be in speech understanding. The identication
of stress or stress accents within an utterance may be useful in predicting deeper semantic
features such asgiven/new information, mood, use of irony or sarcasm etc.
1.4 Overview
This thesis is presented in 5 main chunks corresponding to chapters 3,4,5,6 and 7. First however
a review of background information is given in chapter 2.
Chapter 3 deals with the problem of processing the data in the Spoken English Corpus in such
away that statistical information may be gathered automatically. It describes an algorithm and
software that was devised to cross{reference between the prosodic annotations with the word class
tag information.
Chapter 4 uses the results of this cross{reference to extract various statistical measures of
prosody and word class information specically the co-occurrence frequencies of each word class
with each prosodic mark. That is the number of times that words with a given word class tag are
annotated in the corpus with each of the prosodic marks.
Chapter 5 describes and develops a model using the co-occurrence frequencies that can predict
with high accuracy which words (corresponding to their word class tags) should be stressed (as
4

opposed to left unstressed) in the speaking of an utterance. The predictions are made only on the
basis of the word class tags the actual words are not relevant to the operation of the model.
Chapter 6 builds upon the success of the model rening it to include stress accents in its
predictions instead of just making the stress/unstressed distinction.
Chapter 7 presents a review and analysis of the models which demonstrate their potential
and limitations. It demonstrates how well each word class tag is modelled and how well each
prosodic mark is modelled. It shows that although stress prediction works well, the prosodic mark
prediction model is somewhat constrained in that it does not model the dierence between stress
accents well it has a predisposition to make use of the fall stress accent. And nally, future work
possibilities are identied.
5

Chapter 2
Background
2.1 Introduction
This chapter provides background information on prosody and syntax, Spoken English Corpora
and the computational uses of prosody.
2.2 Prosody
The term prosody comes from Ancient Greek, and the study of it dates from that time if not
earlier. Robins[Rob67] says:
Apollonius's son, Herodian, is best known for his work on Greek accentutation
... covering the eld of the prosodai ... The prosodai were described in more detail
by later scholiasts and came to include the distinctive pitch levels symbolized by the
accent marks on written words ... It is interesting to see the Greek word prosoda
covering very much the range of phonetic phenomena to which the term prosody has
been applied ...
But Crystal[Cry69] (p. 20{21) points out that no major work was done on prosody until the 16 th
Century.
6

It is generally agreed that the earliest discussion of melody in spoken English is
that of John Hart in his Orthographie, and The opening of the unreasonable writing of
our Inglish toung. The former, published in 1569, has a long section on intonation (see
Danielsson, 1955, pp. 199{201) ... In The opening of the unreasonable writing of our
Inglish toung (1551, xx164{5 see Danielsson, 1955, pp. 147 .), there is an attempt to
outline the nature of stress in English ...
Following Hart's work, Crystal (p. 22) says, was:
Butler (1633), who provides the rst connected discussion of the two main English
tunes ... [and Flint (1740)] which involved reference to stress, there was no specic
study until Steele (1775) and Walker (1787).
A review of the history of English prosody is given in Crystal[Cry69] sections 2.5 to 2.7.
However there is not much work of relevance to this thesis until after the late fties when systems
of tonetic stress marks and the tone unit were given substance by O`Connor and Arnold[OA61].
The particular emphasis of this thesis is on machine readable copora which have not existed until
very recent years.
2.2.1 Denition of prosody
The terms used in the study of prosody are often ambiguous and confusing (for example British
researchers tend to use the term prosodic whereas American researchers tend to use the term
suprasegmental). In fact some researchers tend to talk about prosody as a term synonymous with
intonation or intonation and intensity. The purpose of this section is to provide denitions of the
main terms and particularly those relevant to this work. In all cases British English is the only
language under consideration unless stated otherwise.
Prosody
Prosody is the term used to describe those features of speech that are considered to be nonsegmental
usually agreed to include intonation, stress, loudness, rhythm, tempo and voice quality.
7

These contrast with segmental aspects of speech suchaswords, syllables or phonemes. Prosody
is often used as an alternative termtointonation. Hence prosody is the superordinate term used
to cover all of the above aspects which will be described individually in the sections below.
In the work presented here prosody is used primarily to indicate stress and intonation and to
a lesser extent loudness. No consideration is given to aspects of rhythm, tempo and voice quality.
Suprasegmental/nonsegmental
According to Roach[Roa92] (p. 105) \A term invented to refer to aspects of sound such asintonation
that did not seem to be properties of individual segments ... much British work has preferred
to use the term prosodic instead." Crystal[CQ64] (p. 341) also lists plurisegmental and superx
as alternative terms.
Intonation
Intonation is the patterns of changing pitch ofvoice (or melody) over an utterance used to convey
information. However, as Roach (p. 56) points out it is often used in a \broader and more popular
sense, [as] equivalent to prosody, where variations in such things as voice quality, tempo and
loudness are included."
Intonation is used by speakers to convey emotions and attitudes and (p. 57) \interesting relationships
exist in English between intonation and grammar" suggesting that intonation also plays
a role in guiding the listener through the structure of the utterance
Descriptive frameworks have been developed to describe the changing pitch movements. The
tone unit is considered to be the basic unit of prosody and intonation and is the approach most
widely used in Britain. See section 2.2.4.
Pitch
A sound with a periodicity issaidtohave a pitch. Speech is considered to be periodic although
strictly speaking there are minor variations between cylces. Pitch is sometimes considered equivalent
to fundamental frequency (F0) but this is not the case the fundamental frequency is the
8

Figure 2.1: Waveform for the vowel e with one cycle or pitch period marked.
acoustic counterpart to pitch. Pitch is a complex auditory perception. It is possible to perceive a
change in pitch when the fundamental frequency is xed but signal intensity is slightly varied an
increase in intensity produces a drop in pitch as noted by Lehiste (p. 67)[Leh70].
Under most conditions pitch remains closely related to the fundamental frequency. Figure 2.1
shows the waveform for a vowel with one cycle or pitch period indicated. The more cycles there
are per second the higher the perceived pitch. The relationship is not linear however and the mel
scale relates pitch to frequency (see Lehiste p. 65). Signicant changes or contrasts in pitch give
rise to pitch accents.
Accent
Accent refers to a prominence sometimes called a pitch accent. This is distinct from stress in that
stress is more generally used to refer to other types of prominence including loudness, length and
sound quality.
Accent may also refer to a particular way of pronouncing. For example two English people
may say the same sentence but one may speak with Received Pronunciation whilst the other may
speak with a broad Yorkshire accent.
Stress
Stress is the term given to any form of prominence of syllables.
Sentence Stress is the most
prominent word in a sentence and word stress is the stress pattern on the syllables within a word.
The position of stress within a word can determine its meaning for example REfuse and reFUSE.
Stress is a complex topic and not completely understood. Components of stress include pitch
prominence, increased articulatory eort, loudness, and syllable lengthening.
9

The number of degrees (or levels) of stress are a subject of disagreement yet it seems that no
more than three levels (unstressed, weakly stressed, and strongly stressed) are all that is necessary.
See Fudge[Fud84] for more information.
Loudness
Roach (p. 68) has the following to say on loudness:
[Loudness is the] auditory impression of the amount of energy in sounds. We all use
greater loudness to overcome dicult communication conditions ... and to give strong
emphasis to what we aresaying, and it is clear that individuals dier from each other
in the natural loudness level of their normal speaking voice. Loudness plays a relatively
small role in the stressing of syllables, but it seems that in general we do not make
very much use of loudness contrasts in speaking.
Crystal (p. 215) adds that it corresponds \to some degree with the acoustic feature of intensity
(measured in decibels (dB))" and he goes on to note that \other factors than intensity may aect
our sensation of loudness, e.g. increasing the frequency of vocal cord vibrations ..."
Rhythm
The timing and distribution of events in speech. Roach (pp. 93-94) notes:
An extreme view (though quite a common one) is that English speech has a rhythm
that allows us to divide it up into more or less equal intervals of time called `feet',
each of which begins with a stressed syllable: this is called the stress{timed rhythm
hypothesis.
Crystal (p. 307) denes rhythm as \the perceived regularity of prominent units in speech".
Speech Rate/Tempo
Simply the speed of speaking or rate of articulation measured in, for example, syllables per minute.
Speech rate can be modied (from a speaker's normal speech rate, within certain ranges) for
10

semantic or emotional/attitudinal eects.
Pitch Range/Tessitura
The extent in pitch (between lowest and highest pitch) which a speaker usually uses in normal
speech. This may be extended or shifted for semantic or emotional/attitudinal eects.
Key
Crystal (p. 200) denes Key in the following way:
A term used by some sociolinguists as part of a classication of variations in spoken
interaction: it refers to the tone, manner, or spirit in which a speech{act is carried out,
e.g. the contrast between mock and serious styles of activity ...
However, Roach (p. 61) says that key \has generally been used simply to indicate a rough location
within the pitch range" and that the terms high key and low key have been used to describe the
fact that sometimes a speaker will make more use of the higher or lower part of their pitch range
usually as a result of the emotional content of what they may besaying. See Brazil et al[BCJ80]
chapter 2 for a more comprehensive description.
Voice Quality
Distinctive characteristics of a person's speech such a breathiness or creekiness are aspects of voice
quality. Speakers do, however, introduce variations in voice quality for particular purposes. For
example: speaking in a soft voice to indicate sympathy or a harsh voice to show anger. Voice
quality isbeyond the scope of this thesis but see Laver[Lav80, Lav72] for a more authorative
description of voice quality.
Juncture
Crystal (p. 197) classies juncture as \phonetic boundary features which demarcate grammatical
units ..." and Roach (p. 60) describes it as the \way one sound is attached to its neighbours"
11

and \where one found in continuous speech phonetic eects that would usually be found preceding
or following a pause, the phonological element of juncture would be postulated". Crystal
demonstrates with an example:
Word{division, for example, can be signalled by a complex of pitch, stress, length and
other features, as in the potential contrast between that stu and that's tough.
Roach lists some other examples: cart rack/car track, pea stalks/peace talks, great ape/grey tape.
2.2.2 Auditory versus Acoustic Prosody
Research into prosody usually falls into two camps: auditory or impressionistic/subjective and
acoustic.
Acoustic research concentrates upon physical phenomena such as can be measured
and would for example concentrate upon (for intonation, for example) the acoustic correlates
such as fundamental frequency whereas an auditory approach would be more concerned with the
perceptual phenomena such aspitch rises and falls.
For computer based work it is dicult to follow an auditory approach because of the problems
inherent in modelling complex perceptual eects. For example it is relatively easy to measure F0
but it is not possible to measure pitch since it is a perception. See [Leh70, Cry69, Lav80, BCJ80,
Fud84] for relevant work.
2.2.3 Functions of prosody
The functions of prosody are diverse and opinions are divided between emotion and attitude signalling
versus grammatical and lexical information. See [Cru86, Cry69] andLaver[Lav94](pp.494-
498) and Roach[Roa91](p.163). It seems reasonable that prosody performs both tone unit segmentation
correspond with some major syntactic constituents. See section 2.6. Lea[Lea80] has
shown that there is a relationship between word class and stress. Prosody also directs attention
or focus and can signal new or old information as well as contrasting or correcting and echoing
information. For example see section 1.3.1.
12

2.2.4 Representation of prosody
An old transcription system often used referred to as \interlinear tonetic" represents intonation
as a sequence of dots or dashes (with curves representing stress accents) between two horizontal
lines (see [Roa91, Cru86] for examples). This is not a convienient representation scheme for use
in machine readable processing of intonation though.
Cruttenden[Cru86] comments:
there have beentwo alternative approaches to the analysis of English: the older British
`whole tune' approach which describes the overall tunes associated with sentences ...
and which does not therefore have a concept of nucleus and nuclear tone ... [and the]
American approach involving pitch levels and terminal junctures.
The standard British approach has been the tone unit, with tonic stress movement markers
placed upon the syllables upon which a pitch accent starts. The structure of a tone unit is dened
as
(pre{head) (head) tonic stress (tail)
The tonic stress is the only element that is required the others being optional. Laver[Lav94](p.492)
says
Any legitimate utterance of English is made up of one or more intonational phrases,
and each intonational phrase contains one intonational nucleus at which one of the
possible nuclear tones is chosen.
If any words precede the tonic stress they comprise the pre{head, any words which are stressed
(but without accent) form the head. Words following the tonic stress are the tail. Typical tonic
stress accents include rises, falls, fall{rises, and levels.
In British writing another transcription system indicates the tune in a tone unit by anumber
at the start of each tone unit with diacritics to indicate variations.
One of the problems with the above system comments Cruttenden[Cru86](pp.63-64) is the
confounding eect of the various distinctions of pitch range. Recent work, he says, has explored
13

the use of only two level tones (High and Low). He goes on to describe autosegmental intonation
(see section 3.8.1).
Cruttenden reports that the model presented by Pierrehumbert (1980) uses \an LP{type metrical
representation of the text ... and secondly a tune represented by a sequence of high (H) and
low (L) tones ... the model constructs an underlying representation for tunes of English intonation
and a set of rules which transmutes such tunes into actual patterns of fundamental frequency".
Refer to section 3.9 of Cruttenden[Cru86] for a summary.
Another transcription commonly used until recently in American writing used one of a number
of pitch levels at crucial change points in a contour.
Crystal[Cry69] denes the most comprehensive prosodic and paralinguistic transcription which
has been used in the London{Lund Corpus of Spoken English (see below).
2.3 Syntax
Although the emphasis of this thesis is upon the automatic generation of prosodic annotations
from their relations to word class it is necessary to include a few denitions on syntax.
2.3.1 Denition of Syntax
Syntax is the grammatical arrangement ofwords showing their connection and relation or a set of
rules dening how words may be combined.
PartofSpeech
The part of speech ofaword is its grammatical identity such as noun, verb, adjective, conjunction
etc. Most dictionaries will list the parts of speech for words although in corpus based natural
language processing there are usually many subdivisions of the basic classes given above. For
example in the system used throughout this thesis there are 29 divisions within the noun class.
Some words may have more than one part of speech which gives them dierent meanings in
dierent circumstances. For example \record" as a verb is \to make a copy of something" whilst
14

as a noun it is \a disc of plastic from which music may be played".
Wordtag
Awordtag is a symbol used to annotate a word in a sentence (usually contained within a corpus)
to indicate the part of speech ofthatword. For example NP1 is the wordtag for a singular proper
noun.
The symbols for wordtags vary amongst authors but in all cases within this thesis the word
tags used are from the CLAWS4 (see section 2.5.3) part of speech tagging system. See appendix B
for a list of the tags used.
Note that some of the wordtags are very highly constrained for example the wordtag VBZ is
only used for is or 's.
Parsetree
A parse tree is a tree{like structure that shows the inter{relationship between parts of speech
and shows phrase and clause structures within a sentence. In this example wordtags immediately
follow the word they belong to with a separating underscore character. Thus right JJ is the word
right with wordtag JJ which means adjective in this context. For example (SEC section A09
sentence 9):
[N They_PPHS2 N][V are_VBR [J right_JJ J]
[Ti to_TO be_VB0 [J sceptical_JJ J]Ti]V]
The square brackets indicate phrase and clause structures and come in matching pairs so the [J
bracket before the word right matches the J] bracket just after it. In this case They is a noun
phrase, right and sceptical are adjective phrases, to be sceptical is a clause with innite
head and are right to be sceptical is a verb phrase. It can be seen that with the nested
matching brackets the above sequence could be drawn as a tree. For a full list of phrase and clause
labels refer to appendix B.
15

Treebank
A treebank is simply a collection of parsetrees for each of the sentences in a text.
2.4 Approaches To Natural Language Processing
In this section I will briey outline some of the wider ranging approaches taken to natural language
processing (NLP) which dier from the probabilistic corpus{based approach taken in this thesis.
Roger Schank argued that whatever information is encoded in the organisation of language can
be extracted directly without building an intermediate representation(p.32)[MA91]. That is, it is
possible to process language without a system of grammar.
Gazdar's et al Generalised Phrase Structure Grammar (GPSG 1 )[GKPS85], on the other hand,
contains specic rules about possible and legal structures of the kind which Schank avoids. However,
approaches such as Gazdar's also contain mappings between syntactic rules and rules for
semantic interpretation. So, it is easy to view any structure syntactically and semantically.
Both of these approaches to NLP use a system of rules. Rules for representing legal and illegal
structures and rules for mapping between interpretations of structures.
Computer programming languages such as LISP and PROLOG (with its unication mechanism)
have been instrumental in the design of systems such as GPSG. See Gazdar and Mellish's
book on NLP in PROLOG[GM89] for an example.
Representation of meaning was approached by Schank by dening a set of \atomic" primatives
in terms of whicheverything else was dened. His theory of conceptual dependency was an attempt
to nd a minimal set of semantic primatives which can be used for the interpretation of all natural
language texts. He qualied this by arguing that any two texts which have the same meaning
should be represented in the same way. For example:
John loves Mary.
1 GPSG is a framework for writing fully explicit formal grammars for natural languages. It is a notationally
elaborated varient ofcontext{free phrase structure grammar.
16

Mary is loved by John.
Should both have the same representation 2 . See, for example, Schank [Sch73]. Wilks's[Wil78]
theory of preference semantics is similar but uses a much larger set of primatives.
In contrast to the symbolic rule{based models mentioned above, connexionist approaches such
as that of Waltz[Wal89] are based upon neural network models of language.
Waltz lists the
advantages of connexionism as:
Connectionist systems exhibit non{trivial learning ...
[they] can be made fault{
tolerant and error{correcting, degrading gracefully for cases not encountered previously.
... Connectionist architectures also scale well ...
He goes on to point out that:
In contrast, systems based on logic, unication and exact matching are inevitably
brittle.
Similar to neural networks are Hidden Markov Models (HMM) (see Rabiner[Rab90]) which are
also used in non{symbolic, non{rule based approaches to NLP.
There are then, two main approaches to NLP:
1. rule{based symbolic systems
2. non{symbolic connexionist or stochastic systems.
The work in this thesis, whilst it does not used neural networks nor HMMs, falls into the latter
category.
2.5 Spoken English Corpora and Prosodic Annotation
There are three main prosodic annotation schemes used in machine readable speech corpora: the
system used in the SEC (standard British prosodic annotation) or variations on it which are based
2 Some linguists, however, would question whether these sentences are actually the same
17

upon O'Connor and Arnold[OA61] the system used in the LLC which follows Crystal[Cry69] and
the ToBI system[SBP + 92] derived from Pierrehumbert[Pie87]. See below for information on these
corpora. The reader is referred to the relevant references for information regarding the annotation
information in corpora other than the SEC.
2.5.1 London{Lund Corpus
The London{Lund Corpus of Spoken English (LLC) derives from two projects: the Survey of
English Usage at University College London launched in 1959 by Randolph Quirk and the Survey
of Spoken English launched in 1975 by JanSvartvik at Lund University.
The LLC contains written as well as spoken material including surreptitiously recorded material.
Texts have been analysed grammatically and have a prosodic/paralinguistic analysis (following
Crystals conventions[Cry69]) which are held on typed cards. Only a fraction of this prosodic
paralinguistic analysis is available in a machine readable form. Greenbaum and Svartvik[Sva90]
state that:
The basic prosodic features marked in the full transcription are tone unit boundaries,
the location of the nucleus (ie the peak of greatest prominence in a tone unit), the
direction of the nuclear tone, varying lengths of pauses, and varying degrees of stress.
Other features comprise varying degrees of loudness and tempo (eg allegro, clipped,
drawled), modications in voice quality (pitch range, rhythmicality and tension), and
paralinguistic features such as whisper and creak. Indications are given of overlap in
the utterances of speakers. The full transcription and the grammatical analysis are
available only on the slips at the Survey of English Usage at University College London.
The (machine readable) reduced transcription includes tone units, onsets, nuclei, nuclear tone
direction (falls, rises etc.), boosters, pauses (2 degrees), and stress (2 levels).
18

2.5.2 Polytechnic of Wales Corpus
The Polytechnic of Wales Corpus (POW)[FP80] is not really suitable for analysis within this thesis
since it is neither machine readable (being collected in the days prior to word processors) nor is it
of suitable material since it is a corpus of child speech. It is however worth a mention because it
contains prosodic annotations as well as a full grammatical analysis.
It is worth noting that the original recordings have been rescued by Clive Souter and have
now being copied to digital audio tape (DAT). The four volumes of transcripts may one day be
scanned to machine to make thePOW corpus machine readable.
2.5.3 SEC/MARSEC
The Spoken English Corpus (SEC)[KT88] (also known as the Lancaster/IBM Spoken English
Corpus) was compiled between 1984 and 1985 at the Unit for Computer Research on the English
Language (UCREL), University of Lancaster, and the Speech Research Group at IBM UK Scientic
Centre, Winchester. As the name implies, the SEC is a corpus of spoken British English
(taken mainly from BBC Radio 4 broadcasts) which isavailable as lexicographically transcribed
texts (with and without punctuation), as part of speech annotated texts, and as prosodically annotated
texts. All annotations were produced manually with the exception of the part of speech
annotated texts which are semi{automatically produced. In addition, as a parallel resource, there
is a treebank version of the corpus. The SEC is available through the International Computer
Archive of Modern English (ICAME).
Together these form a very rich source of information and all are in a machine readable format.
A potential drawback but one solved in chapter 4 is that the diering versions of the corpus exist as
separate entities that have evolved independently they are not related to each other except by the
fact that they cover the same speech material. See appendix A for a description of the contents of
the corpus. For more comprehensive information refer to the original corpus documentation[KT88].
The corpus size is approximately 52000 words, which is quite small by modern computer
corpora standards. Although this may be a problem the corpus is so richly annotated as to make
19

it a very desirable resource.
The Machine Readable Spoken English Corpus (MARSEC)[RKVA94, GAR92] is an extension
to the SEC in which the original acoustic data of the corpus has been digitised and made available
on CDROM. To complement this fundamental frequency, RMS energy, time aligned segmental
transcription and syllabic divisions have been added.
As a direct result of work done in this thesis cross{reference between the prosodic, part of
speech, treebank, syllabic, and segmental transcriptions have been produced which form part of
the Leeds (UNIX/waves based) version of the corpus | although not yet released. The cross{
reference allows direct links to be made from any point in the corpus between any two (or more)
versions of the corpus annotations (including acoustic signal, F0 and RMS energy).
The part of speech annotations in the corpus were assigned at the University of Lancaster
using their CLAWS[GLS87, Atw83] tagging program which was rst developed between 1981 and
1983 at the Universities of Lancaster, Oslo and Bergen.
The prosodic annotations were produced manually by two expert transcribers 3 using a system
based upon O'Connor and Arnold[OA61]. The corpus was annotated with 16 prosodic marks.
The " and # symbols could be used in conjunction with any of the high or low level TSMs. Of
the tone unit boundaries only one transcriber used the hesitation boundary. A major boundary
existed where there was a pause a minor boundary where there was a boundary without a pause.
Hesitation boundaries were placed in instances where there was a pause but one would not normally
expect to nd a boundary. See table 2.1
2.6 Computational Use of Prosody
Even though prosody is an integral part of the speech act and conveys several types of information
it has hardly been exploited in computational systems such asspeech synthesis and recognition.
Waibel[Wai90] claims that
3 Dr. B. Williams and Dr. G. Knowles
20

" higher than predictable pitch
# lower than predictable pitch
low level
low fall
low rise
low fall{rise
low rise{fall
high level
high fall
high rise
high fall{rise
high rise{fall
stressed but unaccented
k major tone unit boundary
j minor tone unit boundary
* hesitation tone unit boundary
Table 2.1: Prosodic marks used in the SEC/MARSEC
To this day, the prosodic cues in the speech signal, duration, rhythm, intensity, pitch
and stress, are frequently being ignored in the implementation of speech recognition
systems.
and that
Several attempts at using prosodic cues in speech recognition systems have mostly been
limited to aiding syntactic analysis by hypothesizing phrase or clause boundaries (from
pitch excursions) and/or hypothesizing phonemicaly reliable parts of the utterance
(\islands of reliability") from the amount of stress signal.
Klatt has also commented on the little of use of prosody[Kla80, Kla90]:
While relatively little use has been made of prosodic information in most recognition
systems described to date, some ideas for prosodic analysis have been proposed and
tested (Lea, Medress, and Skinner, 1975).
Again the same opinion is expressed by Lea[Lea80]:
If there is one aspect of the information in the speech signal that seems promising and
yet \untapped", it is the \suprasegmental" information ...
21

He goes on to document several prosodic correlates of linguistic structures that have potentially
useful applications in computer speech technologies.
2.6.1 Speech Recognition and Understanding
This section demonstrates that although some work has been done on integrating prosody into
speech recognition and understanding systems there is still much to be gained.
It is commonly agreed that speech recognition can be improved by use of prosodic information.
\Prosodic cues (fundamental frequency, segmental duration, and intensity contour) suggest a
stress pattern for the incoming syllable string and thus could assist in lexical hypothesization"
said Klatt[Kla80, Kla90]. Three types of prosodic knowledge source (duration and rhythm, stress
and intensity) were investigated by Waibel[Wai90] for use in a speech recognition system and he
showed that \dramatic overall improvements" were attained when used in combination with a
speaker{independent phonetic word hypothesizer.
Longuet{Higgins[LH85] comments upon the possible uses that intonation may be put to for
speech understanding. In particular he suggests that contrastive pitch movements may be used
to identify the relative importance of words in an utterance and thus indicate emphasis or new
information. Similarly on the subject of prosodics in speech understanding systems Woods[Woo85]
says that in a speech understanding system it is necessary to have:
the ability to use cues such asintonation and rhythm to predict the possible syntactic
structure of an utterance or to conrm or reject a proposed syntactic structure.
To date none of the speech recognition systems currently available commercially make signicant
use of prosodic information.
2.6.2 Speech Synthesis
Prosody is an important and integral part of speech and speech synthesis systems designers have
tried to model aspects of it such as, for example, rhythm[Isa85] and melody[Ste85] although the
latter notes that (for French) \the gain in intelligilbility is almost zero" but that the speech will be
22

much more \pleasant". He also notes that \not all authors agree that prosody is directly related
to the syntactic structure of a sentence".
The Klatt system (see [Kla87]) has a prosodic processing stage where prosodic contours are
applied in the synthesised utterance taking into account syntactic structure. Word classes give rise
to a potential to aect the fundamental frequency contour, nine of which are actually distinguished
from one another. The relative height of the peak deection of the F0 contour depends upon the
level given to each word class. This however does not correspond to the way in which prosodic
annotations are given to speech. That is: in terms of rises, falls and stresses.
There appears to be no system that can do the conversion between prosodic annotations
(whether of SEC, LLC or ToBI type) and the acoustic signal.
However, see chapter 3 of t'
Hart[tHCC90]. Indeed it is not even known if there is a real acoustic correlate or if the transcriptions
are auditory phenomena. Should such a process become available by the work of others the
work presented in chapters 5 and 6 will be able to generate annotations similar to those in the
SEC and so becomes a link in the path from text{to{speech.
23

Chapter 3
Relating Prosody and Word Class
3.1 Introduction
Comparison of variations in prosody and word class annotations requires that one has that information
for the same utterances. This information is provided in the SEC but not in a unied way.
This chapter addresses the problem of how to cross{reference between the prosodic and the word
class annotations.
3.2 A Source of Data
With any statistical analysis a body of data is needed. For the task in hand this data must be
speech which has been lexically transcribed, tagged with word classes and must be annotated by
experts with prosodic information. Ideally the acoustic data should be available to allow reference
between the annotations and the original speech. This is also preferable for future work where
the relationships between the prosodic annotations and fundamental frequency, RMS energy, and
duration could be investigated[RAng, GAR92]. The data must also be in a machine readable form
since the task of coding such information is highly time consuming.
There are manyspeech corpora available but few (at that time) oered the requirements of be-
24

ing: machine readable, British English, having word class and prosodic annotations and providing
acoustic data. The only obvious candidate: The Spoken English Corpus (SEC)[KT88, GAR92,
Kno88] was, by chance, being updated jointly by Leeds University and Lancaster University to
become The Machine Readable Spoken English Corpus (MARSEC)[RKVA94]. Other corpora are
ruled out for various reasons: the Polytechnic of Wales Corpus (POW) is a corpus of child speech
which has some prosodic annotations but is not in a machine readable form and the London{Lund
Corpus (LLC) has some sections of speech with prosodic annotations however these are mostly
not in a machine readable form and due to the source of the data the acoustic recordings are not
available. The standard corpus does not have word class tags | although these now exist for some
parts.
MARSEC provides in machine readable form the acoustic data along with fundamental frequency
and RMS energy traces, syllabic divisions, segmental time alignment aswell as all the
data that is contained within the SEC (see appendix A) along with the ability to cross{reference
between forms of data. The MARSEC project was not due to nish until the end of this work
hence the cross-referencing described below has proved to be a useful addition to that project.
Other speech corpora are discussed in chapter 2. The SEC and MARSEC are ideal sources for
this work.
3.3 A Need For Processing
In an ideal world the information in the SEC would have been easily extractable. That is: prosodic
information should be directly comparable to word class information and vice{versa. The original
structure of the SEC does not allow for this: word class information and prosodic information
were held in parallel versions of the corpus text.
There are a number of dierences between the two representations of the corpus texts that
hold information on word classes and prosody. A manual of information to accompany the SEC
Corpus[KT88] (sic) explains that, in general, transcriptions were produced from recordings of
25

speech. These (unpunctuated) transcriptions were then used in conjunction with the recordings
to produce the prosodic annotations. Upon occasion it was necessary to alter the transcription
to allow for the appropriate placement of prosodic tone marks. For example, if a prosodic marker
were to be placed on the nal syllable of the word \19" it would be necessary to rewrite \19" as
\nineteen". This generally happened with numbers and acronyms but only where it was necessary
| not in every instance.
Meanwhile the transcriptions were being punctuated and then tagged with word classes using
the CLAWS tagging program. Modications that were made to allow prosodic markers to be placed
were not also applied to the punctuated or tagged annotations. Likewise the representation of the
word classes required changes to be made to the tagged annotation which were not propagated
to other annotations namely the treatment of enclitics such as\won't" (section A01 line 18) and
\don't" (section A11 line 43) which are expanded into \will n't" and \do n't" (notice that this is not
merely a case of splitting the two parts of the enclitic, so the process is not easily reversed without
domain knowledge). This is to allow the insertion of the appropriate word classes. Compounds
(which may be several words long, hyphenated or not) may be classied as a single word class
and are therefore treated as a single word. Possessives such as \England's" (section A01 line 28)
are split like enclitics into \England" and \ 's" although it should be noted that the tagging
scheme used in the original SEC (CLAWS1 ) did not do this and this is only applicable to the
tagging scheme used to tag the parsed annotated corpus (CLAWS4 ). Finally minor dierences
were introduced by changes of case for some words (which, the author understands, to have been
caused by some rule{based mechanism in CLAWS).
Two very obvious dierences between the annotations are that the tagged annotation contains
punctuation and word classes whereas the prosodic annotation contains tone{unit boundaries and
prosodic tone and stress markers.
The annotations were preceded with headers detailing the
source, speaker(s) and other details. Comments enclosed within square brackets detailed such
information as speaker changes or extracts omitted. The original SEC tagged les retained this
information and tagged it as if it were a part of the original script which is not true. Care must,
26

therefore, be taken to ensure that these comments are not taken as data.
Since the treebank 1
contained word class information and phrase bracket information it was
desirable to allow for use of this data yet attempts to match the word class data and the treebank
data with the prosodic data proved too arduous because of changes that had been made (for better
or worse) to the word class data. This meant that although it was possible to match the word class
or treebank data to the prosodic data, it was dicult to resolve the dierences in punctuation
between the word class and treebank versions. A decision was made to drop the word class data
and use the treebank data exclusively. There is a distinct advantage to this since the treebank
was tagged using CLAWS4 | a more advanced tagging system to that used to tag the word class
version.
All these factors give good reason why it has not been an easy task to cross reference data in
the prosodically annotated version of the corpus with the word class information in the corpus.
They also indicate that a fairly complex cross{referencing process is necessary. This is detailed in
the remainder of this chapter.
3.4 Cross Referencing
The structure of the corpus and the problems described above meantitwas not possible to
answer question such as \what word class has a given stressed word in the corpus been tagged
with" or \list all the proper nouns sorted according to the type of prosodic marks they have".
Cross{referencing between word class and prosodic information was an essential step.
To this
end software was written that would match upword{by{word the two data sources. Before the
cross{referencing can begin it is necessary to preprocess the corpus data to transform it into a
useful format. Figure 3.1 shows an extract of the prosodic annotation format and gure 3.2 shows
an extract of the treebank annotation.
See appendix F for the source code.
trees.
1 The treebank is a further version of the corpus containing word classes (assigned using CLAWS4) and parse
27

The preprocessing (acomplished with UNIX 2 tools) produces two les from each of these formats.
The rst two les produced from the prosodic annotation contain one word per line (here
we dene \word" as any sequence of characters delimited by whitespace). In one le the words
retain their prosodic annotations and in the other these are removed just leaving the word. Tone
unit boundaries are treated as words in this context and appear on a separate line. Comments
contained in square brackets are discarded. The second two les produced from the treebank are
similar: one le contains each word (separated from the word class tag) and the other contains
the word class. Phrase bracket and sentence numbers are discarded. Punctuation is treated as a
word in these les (a punctuation symbol being given the word class tag of itself). As a further
aid to the following stage the case of letters in the two word les is converted into lowercase where
appropriate.
This preprocessing stage is doubly useful in that it not only simplies the cross{referencing
stage but it also insulates the body of the process from the potentially variable annotation formats.
Hence by changing the preprocessing stage this software may be used with alternative annoatation
formats.
Before the treebank format was adopted as a source of word classes the word{class
tagged version of the SEC was used. This change was acomplished with a minor change to the
preprocessing stage.
The next stage is the use of the program ttalign (see section F.2). The program works like this:
the corresponding entries are read from the two les produced by the preprocessing stage from
the prosodic annotation and similarly for the two les produced from the treebank. The program
then compares the word from the prosodic annotation with the word from the treebank. If these
words match an output line is generated from the input. As a special case punctuation is treated
as equivalent to tone unit boundaries where they coincide (remember that tone unit boundaries
only exist in the prosodic annotation and punctuation only exists in the treebank). Two other
possibilities are that the prosodic annotation word is a tone unit boundary and the treebank word
is the next word after the boundary. In these cases a ller symbol (fTUg) is inserted to match with
2 UNIX is a registered trademark of UNIX System Laboratories.
28

the tone unit boundary on the output line alternatively the treebank word may be punctuation
whereas the prosodic annotation word may be the next word after the punctuation. As above a
ller symbol (fPNg) is inserted to match the punctuation on the output line. The symbol fTUg
stands for \a tone unit boundary that does not coincide with any punctuation" and the symbol
fPNg stands for \a punctuation symbol that does not coincide with a tone unit boundary".
If none of these situations is true, that is: if the two words do not match and neither is a
punctuation or tone unit boundary then the mismatch handling routine is called. In the mismatch
handling routine an output line is generated from the mismatching entries and new input is read.
If these words now match then it is assumed that the previous mismatchwas an error in the corpus
or that the words were dierent representations of the same thing e.g. \nineteen" and \19". In
this case another output line is generated and processing continues as normal.
If the new input words do not match atwo way lookahead stage is entered.
In this the
prosodic annotation word is compared with the next few treebank words and the treebank word
is simultaneously compared with the next few prosodic annotation words until a match is found
in either search oruntil a xed distance ahead has been viewed. If a match is found, depending
upon which stream the match is found in, the program assumes that the mismatchmust have been
caused by either an enclitic or a compound word. As previously noted enclitics such as\won't"
are represented as \will n't" in the treebank. The software will match \won't" with \will" but will
insert a ller symbol (fENg) meaning enclitic which will be matched with \n't". For compounds,
for example, \search and destroy" (section a10 line 43) the software will match \search" with
\search and destroy" and will insert a ller symbol fCPg) meaning compound word which will be
matched with the next two lines: \and" and \destroy".
It it, of course, possible that the two way lookahead will not turn up a match. In this case
an interactive modeisentered that allows the user to specify one of the four ller symbols (fTUg,
fPNg, fENg, and fCPg) which is repeated until the input streams are again in synchronisation (i.e.
the prosodic annotation word matches the treebank word). This is a very rare occurrence and it
was not justiable to add the extra complexity necessary to handle the conditions under which
29

it occurred. This happened when a compound word was followed by a tone unit boundary or an
enclitic was followed by punctuation which did not match punctuation or a tone unit boundary
respectively. Since the lookahead does not check for punctuation or tone unit boundaries it can
easily get confused thinking that, say, the tone unit boundary is part of the compound. For this
reason and to save adding recursive levels of lookahead the interactive modeisentered. When
synchronisation is achieved the user exits this mode and the program continues as normal.
The whole process is repeated for each input line until the data are exhausted. The output
from ttalign is not the end of the cross{reference process: two further stages are necessary. A
post{processing stage handles the tricky problem of what to do if more than one punctuation
symbol coincides with a tone unit boundary. The ttalign program will only match the rst such
punctuation symbol with the tone unit boundary and the remaining punctuation symbols will be
tagged with the fPNg ller symbol which means that the punctuation does not match a tone unit
boundary. This is wrong and to x this the program collate{tu (see section F.3) will nd such
instances and convert fPNg llers into fCTUg ller symbols which stand for collated tone unit (a
single tone unit boundary that matches multiple punctuation symbols). Which of the punctuation
symbols actually matches the tone unit boundary (as opposed to the ller symbol) is determined
by an order of precedence. This usually only occurs with quotes and brackets which are given less
precedence than the other punctuation symbols. With hindsight itwould have been preferable
to use two or three ller symbols such asfCTU1g, fCTU2g, and fCTU3g that are used respectively
with the three dierent types of tone unit boundary. As it stands it is not possible to tell (without
reference to the context) whether punctuation aligned with the fCTUg ller was at a major, minor
or hesitation tone unit boundary. This does not have any eect on the research presented here
but may bear on automatic segmentation of the text into tone units.
The nal stage of the cross{referencing uses the word classes to guide the program align{parse
(see section F.4) while it re-inserts the treebank phrase brackets. Although the phrase brackets
are not actually used here it is hoped that future research may be able to improve on results by
using the contextual knowledge embodied within them. Figure 3.3 shows some example output
30

from the alignment process (here without the phrase brackets). The rst column contains the
word class (see appendix B for an explanation of the code used) the second column contains the
prosodically annotated word and the third column contains the lexical word. Notice how tone unit
boundaries and punctuation are handled. These are not \words" and some researchers choose to
add an extra eld for information such as this placing the punctuation next to the word which it
follows. This approach has not been adopted because of the additional complexity of processing
the data.
3.5 By{Products
The above has also been useful in the production of cross{referencing data in the MARSEC
project[GAR92]. The ability to relate temporal information to the treebank is provided as a direct
by{product of this software. Prosodic annotation words are located in a cross{reference produced
by the above algorithm and the location within the parsetree is identied and a cross{reference
table is produced.
3.6 Summary
The chosen data source, the SEC, contains word class, parsetree and prosodic information which
could not directly be cross{referenced. A semi-intelligent algorithm was used to produce cross{
referencing between these annotations which coped with representational dierences with little
domain knowledge. The results may be used to make direct comparisons between word class and
prosodic annotations.
31

[001 SPOKEN ENGLISH CORPUS TEXT A01]
[In Perspective]
[Rosemary Hartill]
[Broadcast notes: Radio 4, 07.45a.m., 24th November, 1984]
[Transcriber: BJW]
#Good morning k"more news about the Reverend Sun Myung Moon j founderofthe
Uni cation Church j who's currently in jail j for tax evasion k"he was a warded an honorary
de gree last week j by the Roman Catholic Uni versity ofla Plata j in Buenos Aires j
Argen tina k in an nouncing the a ward in New York j the rector of the uni versity j#Dr
Nicholas Argen tato j de scribed Mr Moon as j a prophet of our time k
Figure 3.1: Example of Prosodic annotation format.
SA01 1 v
SA01 2 v
[N Good JJ morning NN1 N] . .
SA01 3 v
[N More DAR news NN1 [P about II [N the AT Reverend NNS1 Sun NP1 Myung NP1 Moon NP1
, , [N founder NN1 [P of IO [N the AT Unication NN1 church NN1 N]P]N] , ,[Fr[N who PNQS
N][V 's VBZ currently RR [P in II [N jail NN1 N]P][P for IF [N tax NN1 evasion NN1
N]P]V]Fr]N]P]N] : : [N he PPHS1 N][V was VBDZ awarded VVN [N an AT1 honorary JJ degree
NN1 N][Nr last MD week NNT1 Nr][P by II [N the AT [ Roman JJ Catholic JJ ] University
NNL1 [P of IO [N la &FW Plata NP1 N]P][P in II [N Buenos NP1 Aires NP1 , , Argentina
NP1 N]P]N]P]V] . .
SA01 4 v
[P In II [Tg announcing VVG [NtheATaward NN1 N][P in II [N New NP1 York NP1 N]P]Tg]P]
, , [N the AT rector NNS1 [P of IO [N the AT university NNL1 N]P] , , [N Dr NNSB1
Nicholas NP1 Argentato NP1 N]N] , , [V described VVD [N Mr NNSB1 Moon NP1 N][P as II
[N a AT1 prophet NN1 [P of IO [N our APP$ time NN1 N]P]N]P]V] . .
Figure 3.2: Example of Treebank format.
32

NNJ BBC bbc
NN1 news news
fTUg j fTONE{UNITg
II at at
MC eight eight
RA o' clock o'clock
fTUg j fTONE{UNITg
II on on
NPD1 Saturday saturday
, j ,
AT the the
MD twenty{ second twenty{second
IO of of
NP1 June june
. k .
DD1 # this this
VBZ is is
NP1 Brian brian
NP1 Perkins perkins
. k .
Figure 3.3: Example output from cross{referencing from section B02.
33

Chapter 4
Preliminary Statistical Analysis
4.1 Introduction
This chapter will explain how statistics were extracted from the corpus and how these statistics
may be used to estimate probabilities for the co{occurrence between prosodic stress marks and
word classes and probabilities for seqeuences of prosodic stress marks and word classes. Together
these probabilities are used in later chapters to build up a probabilistic grammar of prosody that
may be generated from the word classes for an utterance. Other statistics gathered are used to
provide descriptions of the corpus annotations. These are used to provide operational parameters
for the context in which the grammar will work and provide information on the factors of the
prosodic annotation that are not covered by the grammar.
In this statistical analysis of the corpus there were two aims:
to extract information descriptive of the prosodic annotations.
to extract information helpful in relating the prosodic annotation to the syntactic annotation.
The analysis results described and presented here are extracted from the prosodic annotation
and the cross{reference between the prosody and syntax produced in the previous chapter. Only a
subsection of the corpus was used for these analyses because the corpus is comprised of a number
34

of dierent speech styles (see appendix D) and there is a likelihood that unusual prosodic styles
such as found in the poetry examples, for example, may produce spurious results in the statistics.
A subsection of the corpus (of approximately two thirds) was selected roughly corresponding to a
report style. This subsection comprised the categories listed in table 4.1. Category M was reserved
for testing purposes.
Category Style #words % corpus
A Commentary 9066 17%
B News Broadcast 5235 10%
C Lecture type I (general audience) 4471 8%
D Lecture type II (restricted audience) 7451 14%
F Magazine Style Reports 4710 9%
M Miscellaneous 3352 6%
Table 4.1: Categories of the corpus used for analysis
(K).
Categories omitted were: Religious (E), Fiction (G), Poetry (H), Dialogue (J), and Propaganda
4.2 Prosodic Annotation Statistics
Although there is little new information presented in these statistics they are useful in providing
descriptions of the annotations in the corpus and can serve as a metric to compare synthesized
annotations against. Useful statistics to look at are the relative frequencyofeach of the prosodic
annotations (see appendix D) the lengths of tone units and frequencies of prosodic mark bigrams.
4.2.1 Prosodic mark frequencies
Figure 4.1 shows the relative frequency of each of the prosodic marks (including unstressed)
used in the annotation of the SEC. Unstressed words account for 47.1%, whereas rises, falls, fall{
rises and level tones account for 40%. The remaining 12.9% is largely made up of the class stressed
but unaccented. Rise{fall tones are so rare that they are negligible for our purposes. The one or
two instances that actually do occur in the corpus were ommitted hence the count of zero in the
35

Frequency
12801
3511
2564
2297
1528
1511
1200 1158
342 261
8
0
Unstressed
High Fall
Stress (unaccented)
Low Fall
Low Rise
High Rise
High Fall Rise
Low Level
High Level
Low Fall Rise
High Rise Fall
Low Rise Fall
Figure 4.1: Frequency of prosodic marks
histogram.
4.2.2 Tone Unit lengths
3300
3000
2700
2400
2100
1800
1500
1200
900
600
300
0
0 1 2 3 4 5 6 7 8 9 10
Length of Tone-Unit
Figure 4.2: Relative frequencies of tone{unit lengths in terms of numbers of: words with tonic
stress marks words with prosodic marks and words.
The length of tone units (presented in gure 4.2) was calculated by counting the number of
words with a prosodic mark (the dotted line), by counting the number of words with tonic stress
36

marks only (the dashed line), and by counting the total number of words (the solid line). It is, of
course, not meaningful to have a tone unit with zero words but it is possible to have a tone unit
with no TSM or stressed words. The total length of tone units (in terms of words) extends to over
35 words but this trails o to a frequency below 10 after a length of about 15 words. The average
length of a tone unit is about 4 words.
Of these measures of length only the rst two are useful for comparison with the same measures
taken from any prosody synthesis model that is not concerned with the segmentation (into tone
units) problem. The models presented in chapters 5 and 6 are only concerned with synthesis of
stress and prosodic marks annotations and not prosodic boundaries | these are taken as given.
Under those conditions the number of words in a tone unit will not change as the same tone unit
boundaries are used.
4.2.3 Prosodic mark bigram frequencies
or or or or Unstress kjor *
or 34 40 20 128 329 2044
or 44 304 216 1072 2454 4834
or 10 74 25 701 854 1162
or 953 2381 666 2436 5297 3767
Unstress 1235 4939 1524 8210 8453 1463
kjor * 319 1185 375 2953 8437 N/A
Table 4.2: Prosodic mark bigram frequencies
The gures in table 4.2 show the absolute number of instances of each of the bigrams. So,
for example, the frequency of a fall (either high or low) being immediately followed by a tone
unit boundary is 4834 instances. For reasons applicable to the models developed in chapters 5
and 6 the bigrams gures presented here group low and high tones together as well as grouping
the stressed but unaccented mark together with the low and high level tones. This group is often
referred to here simply as stressed. The unstress element refers to words (not syllables) that have
no stress or prosodic mark annotated. It is interesting to note that 60.6% of tone units are ended
with a falling, rising or rise{fall tone and only 11% with an unstressed word supporting the view
37

that tonic stress comes at the end of a tone unit.
It is interesting to note that none of the cells are zero. This shows that a probabilistic approach
to language modelling is essential. A traditional generative approach to developing a \grammar
of prosodic marks" following Chomsky[Cho57] would involve dening a set of rules to generate all
and only \legal" prosodic mark sequences, and disallowing \illegal" sequences. Since all pair combinations
are legal a rule{based grammar derrived from the bigrams alone would not be sucient.
4.3 Cross{Reference Statistics
The statistics here provide evidence for what has commonly been believed about the relationship
between prosodic marks and word classes. In particular it quanties the relationship and by use
of a larger set of word classications than is normally considered it adds detail. The relationship
between punctuation and tone unit boundaries is also covered by showing the frequency of co{
occurrence of the marks in each annotation.
4.3.1 Co{occurence tables
Of particular interest to this research is the frequency of co{occurence of word classes with prosodic
marks. The prosodic marks (known as Tonic Stress Marks or TSMs) fall on the syllable upon which
a pitch movement starts and the behaviour of the prosody between TSMs may be predicted[KT88].
The TSMs therefore encapsulate the essential changes in the prosody.
The SEC (as noted in
chapter 2) marks every stressed syllable with a TSM with the assumption that the nal TSM in
a tone{unit is the tonic stress. It is this approach to the annotation that makes it possible to
produce the co{occurence table for word classes and TSMs. This could not be done with other
prosodic annotations schemes such asToBI[SBP + 92] which have a rigid grammar and individual
elements cannot be extracted from an utterance ignoring the context from where they came. Of
course a co{ocurrence table (or any other statistical model) cannot readily be extracted from
prosodic annotations unless these are machine{readable. The POW corpus was collated before
word processing software was available so the only \models" we can build from the POW must be
38

ased on intelligent observation of the four volumes of printed transcripts. From the cross-reference
produced in chapter 3 this table is easy to produce: for each word class a count ismadeofeach
time it occurs with each of the possible TSMs plus the annotations of stressed but unnaccented
and unstressed 1 .
One problem with this approach is that some multisyllabic words have more than one prosodic
mark within them (for example \ unscien tic" section a01 line 64). Words with multiple prosodic
marks comprise 1.2% of the sub{corpus used for the cross{reference which is small enough to
ignore and assume that all words will have only one TSM. This is an important assumption since
it overcomes the diculties associated with the fact that prosody and stress are usually syllable
based but word class tagging is word based. Compound words are often treated as a single word
(for example \ battle{ marked" (section a02 line 12) is not treated as two separated words \battle"
and \marked" with word classes NN1 and JJ but as a single word with word class JJ) and cases
such as this often disagree with the assumption. This work does not attempt to address this
problem. Further work on the relationships between prosody and compound words looks like it
would be a fruitful avenue to explore, but is outside the bounds investigated here.
Another problem is the use of " and # which may be either used on their own or in combination
with other TSMs. This increases the number of possible (if not actually used) prosodic marks
substantially yet " and # occur very infrequently. The approach here had been to assume that the
eect of " and # is highly semantic, contextual or pragmatic in nature and hence they have been
ignored. Thus there is no distinction made between TSMs marked with a high or low reset and
those without.
Looking at the frequencies of TSMs (gure 4.1) it was noticed that rise{fall tones are also very
infrequent. So infrequent that statistics gleened from the few instances are likely to be in error
hence they have likewise been ignored.
In Summary: with 168 dierent word classes occurring in the sub{corpus sample and a possible
34 dierent types of prosodic annotation per syllable there is a major problem in regards of sample
1 N.B. From here on I will not normally make the distinction between TSMs and stressed but unaccented and
unstressed annotations.
39

sizes. In order to alleviate this problem certain prosodic annotation marks are ignored. These
are higher and lower than expected pitch level markers (" and #), and rise falls which together
account for less than 0.6% of the data but reduce the number of prosodic marks to ten. Even with
this precaution many cells in the co{occurrence table will be zero and correspondingly distribution
probabilities based on the frequencies will be in error.
A table of frequencies is produced (see table D.3). Presented in table 4.3 are the frequencies
for the 64 most frequent word classes.
The same statistics can also be calculated for tone unit boundaries and punctuation symbols
see table E.1.
Although the phrase brackets are also aligned with the word classes and prosodic annotations
they are not actually used in the mappings dened here. Atwell[Atw94] has some ndings which
support the view that phrase brackets do not give much more information than the word class
tags alone.
4.3.2 Ignoring Higher{Level Syntactic structures
There are two good reasons to ignore the parse tree information available in the SEC treebank.
Firstly, there exist accurate word class taggers (e.g. CLAWS) but no parsers of equivalent
accuracy exist[Atw93, Atw94, ASO88, O'D93, Wee94]. It would be much more useful to be able to
predict prosody from word class tags alone since natural language processing systems can predict
these with condence. Accurate parse trees cannot be generated automatically.
Secondly, parse trees are large structures in comparison with individual prosodic marks and the
SEC is not large enough to be able to provide enough examples of given parse tree structures (or
sub{structures) in correlation with prosodic mark sequences. It is very doubtful that any progress
would be made from use of such limited information within the framework of this research.
This is why this thesis concentrates upon mapping only word class tags to prosody. Hopefully
higher level syntactic structures would only be useful for providing contextual or semantic
information or segmenting the utterance into tone units which are all outside the scope.
40

4.3.3 Clustering word classes
Using the data in the co-occurence table it is possible to perform a prosodic{based word class
hierarchical clustering 2 of these word classes. Clustering of non{parametric n{dimensional data
is a dicult task and many distance metrics exist (see Hughes[Hug94]). It is highly likely that an
improvement on the clustering presented here would be possible. Advanced clustering techniques,
however, are beyond the scope of this research.
By drawing a vertical line through the arcs in gure 4.3 the word classes may be divided into
anumber of groups. A line at the far left would give one group, slightly to the right would give
two groups etc. There would be n groups where the vertical line cut through n horizontal lines
| all the arcs to the right of cut bracket all the word classes in each group. For example with
two groups there would be word classes APP$ to DDQ in one group and DB to RG in the other
group.
This leaves the problem of which group each of the other word classes (not in this gure)
belong to. Low frequency word classes have poorly dened co{occurence vectors which make it
dicult to know which cluster a word class would group with. The best that can be achieved is
to inspect the groups for patterns between the word classes. For example one cluster near the
bottom of gure 4.3 has the word classes: NN1, NN2, NNL1, NNT2, NNU, and NNT1. A clear
pattern exists here and similar low frequency tags such as NNL2 could be added to this cluster.
4.4 Summary
In this chapter consideration has been given to the extraction of various measures to aid the
mapping between word class and prosodic annotations in the Spoken English Corpus. In particular
the range and frequency of the prosodic marks has been presented along with their frequency within
tone units. Prosodic mark bigram frequencies have been extracted which may be used to calculate
likelihood scores for sequences of prosodic marks. It was noted that these gures indicate that in
2 The clustering was kindly performed for me by John Hughes using a technique described in Hughes[Hug94]
41

common with general belief the rst word in a tone unit is usually unstressed and the last word
in a tone unit usually carries the tonic stress.
A mechanism was described to cluster word classes into groups based upon their similarity of
co{occurrence with prosodic marks. This has led to the idea of prosodically orientated word class
groups.
The statistics so assembled here are used in chapter 5 to devise a model to generate stress
patterns from word class tagged text.
42

Tag
U/str
APP$ 1 0 2 9 1 8 0 7 6 303
VBDR 0 0 0 2 0 6 0 1 1 90
VHD 0 0 0 2 1 6 0 0 3 75
CC 5 1 1 12 1 18 0 6 57 683
II 7 1 11 25 13 47 1 25 128 1847
IW 0 0 0 4 1 5 0 3 12 157
PPHS1 0 0 1 5 1 3 0 1 5 116
VHZ 1 0 0 3 1 2 0 1 5 79
VBZ 2 1 3 8 1 6 0 8 10 229
EX 0 0 0 0 1 2 0 0 6 79
PNQS 0 1 0 1 1 2 0 0 4 78
PPIS2 0 0 0 0 0 4 0 1 5 96
VBDZ 0 0 0 2 3 4 0 1 11 261
VBN 0 1 0 1 2 1 0 0 5 104
IF 1 0 0 1 1 0 0 0 11 254
PPH1 0 0 1 1 0 0 0 0 16 245
VB0 1 0 1 2 0 1 0 0 9 153
AT 0 0 0 18 1 19 0 3 42 2016
PPY 0 0 0 0 1 0 0 1 1 68
AT1 1 0 0 1 1 3 0 1 12 739
CST 0 0 0 0 0 0 0 0 8 260
II22 0 0 0 0 1 0 0 0 0 70
IO 0 0 0 1 0 0 0 0 7 896
TO 0 0 0 0 0 0 0 0 3 410
CCB 2 0 0 0 0 3 0 1 23 140
NNSB1 2 0 0 1 1 3 0 0 12 60
CSA 1 0 0 4 1 8 0 0 7 72
VM 2 0 5 20 9 19 0 10 17 218
PPHS2 1 0 0 5 2 2 1 6 7 97
VBR 0 0 1 8 4 3 0 2 5 108
VH0 1 0 0 3 3 4 0 5 13 123
CS 3 1 0 12 3 17 0 0 30 92
ICS 1 0 1 9 7 9 0 5 24 65
DDQ 3 0 1 10 7 11 0 1 22 106
DB 0 1 9 18 5 19 2 14 14 8
NP1 91 52 129 191 131 191 37 155 222 117
JJ 68 15 87 263 200 354 37 177 356 145
VVN 67 8 62 107 96 115 16 56 152 60
JB 7 0 3 23 18 28 1 20 40 24
NN 14 3 13 23 14 23 3 6 45 28
VV0 67 7 47 104 90 105 9 47 185 144
RL 8 1 16 13 10 15 3 4 26 15
RR 41 5 28 157 65 130 16 83 132 117
RT 12 2 8 10 7 15 1 11 23 22
DD 0 0 1 13 9 20 0 17 14 23
MD 3 0 4 18 22 27 3 26 20 29
XX 0 1 4 21 6 22 0 8 10 22
MC 12 2 22 78 63 108 4 35 73 50
MC1 4 1 2 10 11 22 0 12 18 18
NNS1 3 3 3 8 9 15 2 5 31 13
VVD 13 10 21 53 47 85 2 28 147 67
VVG 21 3 6 54 51 71 5 20 105 42
VVZ 11 2 9 20 21 33 1 4 54 40
NN1 379 80 373 610 285 348 106 359 810 275
NN2 160 26 172 236 134 130 45 124 364 141
NNL1 15 4 20 23 13 11 2 8 26 19
NNT2 12 1 13 21 4 4 1 7 15 14
NNJ 7 5 6 20 12 3 7 8 30 19
NNT1 15 3 19 32 13 15 4 12 62 35
DD1 5 1 4 38 6 44 1 31 42 113
MF 3 0 2 13 1 10 0 1 16 24
NNO 3 0 2 9 7 4 0 10 14 26
RP 14 4 24 33 11 18 4 9 24 66
RG 0 1 0 14 6 22 0 5 19 52
Table 4.3: Co{occurence table for 64 most frequent word classes.
43

APP$
VBDR
VHD
CC
II
IW
PPHS1
VHZ
VBZ
EX
PNQS
PPIS2
VBDZ
VBN
IF
PPH1
VB0
AT
PPY
AT1
CST
II22
IO
TO
CCB
NNSB1
CSA
VM
PPHS2
VBR
VH0
CS
ICS
DDQ
DB
NP1
JJ
VVN
JB
NN
VV0
RL
RR
RT
DD
MD
XX
MC
MC1
NNS1
VVD
VVG
VVZ
NN1
NN2
NNL1
NNT2
NNJ
NNT1
DD1
MF
NNO
RP
RG
Figure 4.3: Hierarchical clustering of 64 most frequent word classes.
44

Chapter 5
Automatic Stress Annotation
5.1 Introduction
In this chapter it will be shown that the placement of stresses 1
on words in an utterance may
be largely predicted from word class classications. This chapter is largely based upon the work
presented in Arneld[AA93].
Chapter 6 will expand on the ideas presented here to show how prosodic annotations may be
calculated from word classes. The intention, as pointed out in section 1.3.1, is not to produce
predictions that exactly match the annotations in the corpus but to generate annotations that will
act as a baseline annotation which may be built upon by semantic and contextual processes.
It is possible for a sentence to be stressed in dierent ways in dierent texts (contexts). A
predictor based on sentence{syntax, without any model of \text grammar" or inter{sentential
cohesion cannot hope to work perfectly. This leads to a problem of evaluation if the predicted
stress is dierent from that in the corpus | it need not necessarily be wrong.
There are a number of problems associated with this task (which are examined below):
1 For the purposes of clarity in writing I will refer to a stressed syllable or word here as one that has either a
Tonic Stress Mark or is classied as being stressed but unnaccented. Unstressed refers to words or syllables which
have no prosodic annotation (In some cases words are annotated solely with pitch resets. These are ignored in this
research and such aword would be considered unstressed). In general I refer to stress marks as meaning either of
the above, and not a syllable which isstressed.
45

Stresses are normally associated with syllables, not words.
How does one decide which syllable stress will fall on
Enclitic words have more than one word class.
Compound words have a single word class but multiple words.
A basic assumption of this research has been that it will only deal with at most one stress
mark per word. This is not the case in reality by examining the corpus it can be seen that this
assumption holds for 98.8% of the words in the sections of the corpus used (see section 4.1). The
assumption is such a useful one to make because it simplies the mapping problem to mapping
between a single word class (in most cases) and a single stress mark per word. In words that
feature more than one stress mark the most prominent stress takes precedence, in this analysis.
The second problem follows on from the rst if a stress mark is to be placed (upon a syllable)
within a word, which syllable should it go on This is not a problem tackled by this research
because of a second underlying assumption: that the stress mark will go on the syllable marked
as that carrying the primary stress in a dictionary. According to Fudge[Fud84]
in English, the syllable singled out in a given word is nearly always the same one,
irrespective of the context.
He notes two types of exceptions (i) cases where the word has not been properly perceived by
the hearer and (ii) certain types of phrase require a shift in word{stress. The problem would
therefore be solved by acombination of dictionary lookup using a machine readable dictionary
or lexicon (such as the forthcoming edition of The English Pronouncing Dictionary[RH95]) and
a rule{based approach ascovered by Fudge. Several machine readable dictionaries and lexical
databases commonly used for natural language processing research (e.g. LDOCE, OALD, Collins
English Dictionary) include stress assignment information.
Enclitics (which are pronounced as single words) have more than one word class because they
are formed from two (or more) words that become joined. For example \can't" is \can not" and
46

\won't" is \will not". Two possibilities exist to deal with mapping between two (ormore)word
classes and a single word: (i) treat enclitics as a special case in the stress prediction stage or (ii)
as a special case in the placement of stress marks on syllables stage. The dierence here is either
some additional complexity in the prediction model or some simple rule{based approach inhow
to deal with placing more than one stress mark on the same syllable. Because of limited data on
enclitics being available in the corpus the former approach would be dicult to implement and
mayhave the aect of blurring the word classications of enclitics. Toavoid this potential problem
the latter approach is assumed: a prediction is made for each part of the enclitic and these are
combined at a later stage with the rule that the most prominent stress mark that is assigned to
any part of the enclitic is taken as the stress mark for the whole enclitic. If it is a multisyllabic
enclitic the stress mark will be placed on the primarily stressed syllable as indicated above.
Compounds unlike enclitics suer from the problem that a single word classication is given
for a phrase that maycontain several words. For example \search{and{destroy" may be classied
with a single word class. This will give a single stress mark prediction but in reality more than
one word may be stressed.
In addition it is dicult to say whichword should take the main
stress. In eect what is needed is a lexicon of compound words that list primary stress and gives
rules for assignment of stress marks to the other words/syllables. Compound words are a special
and dicult case that are not dealt with here. It would be outside the scope of this research to
attempt to handle them eectively. Hence, within the constraints of the predictor model used, it
will not be possible to predict stress marks for compound word sequences. However, Fudge[Fud84]
(chapter 5) gives rules to deal with compounds.
5.2 Stress Prediction
The study (described in this chapter and extended in chapter 6) concentrates upon building a
stochastic grammar model of stress based upon word class and the prosodic mark co{occurrence
table.
47

If we can collect a number of \measures" of the relationship between word classes and prosodic
marks we can combine these measures together. Each diering measure of likelihood of relationship
forms a constituent of the overall measure of relationship. Using a numberofsuch constituents
to relate one entity (suchasword class) to another (prosody) is what Atwell[Atw83] has called
constituent likelihood.
In the case of this research the measures are probabilities (or estimates of) of co{occurrence
between word class and prosodic marks.
In the model developed here the distinction between dierent tones will be ignored and only two
types of annotations (stress markers and unstressed markers) will be considered. Stress markers
are considered to be any of the tonic stress marks or stressed but unaccented marks whereas
unstressed markers are considered to exist on words which have no prosodic marks whatsoever or
only have marks " or # although if these exist in combination with a TSM the word is considered
stressed. With the assumptions made above there will be no more that a single stress occurring
within each word one can look at a sequence of words as a sequence of stress and unstress markers.
Each word is either stressed (i.e. contains a stressed syllable) or unstressed (no syllable is stressed).
Since this is a binary string, for a sequence of n words, there would be 2 n possible sequences.
As an example: a three word utterance (\at Ford motors" (section B04 line 51)) would have
eight possible stress patterns where each word is in one of two states.
1 at Ford motors
2 at Ford motors
3 at Ford motors
4 at Ford motors
5 at Ford motors
6 at Ford motors
7 at Ford motors
8 at Ford motors
Here
indicates (some type of) stress on the word (others being unstressed). If we wish to assign
48

stress to the appropriate words in an utterance we needtondwhich of the possible 2 n sequences
are valid or acceptable. One way of doing this is to assign a score to each sequence and pick the
highest scoring sequence as the pattern for the utterance. The scores being designed such that
the \baseline" stress patterns generate the best scores.
5.2.1 Search Mechanism
For limited sizes of n the number of sequences is small enough to do a global search. If n were large
or if we were dealing with more that two possible annotation types for each word then it would
be necessary to use an alternative search methodology to cut down on the computational load.
For example for a 10 word sequence with two annotation types there are only 1024 possibilities
but if there were, say, ve annotation types this number rises to 5 10 = 9765625 which is too large
to search exhaustively in reasonable time. Alternative search possibilities exist (see section 6.3.4)
where more annotation types are considered.
5.2.2 Scoring
How does one assign scores to the sequences
Various factors appear to be relevant including
semantics, pragmatics, word class and context and clearly all of these could be used to provide
components of the score. Since the aim of this research is to demonstrate a relationship between
syntax and prosody we will only use measures derived from word class and context and will not
attempt to implement any measures based upon semantics or other relevant factors.
One could try the following formula for scoring each possible sequence of length w words
function a n gives the stress{state (or annotation) of word n (i.e. either stressed or unstressed)
function w n
gives the word class at word n into the sequence and function S(p q) gives the
probability (or likelihood) that word class p would have annotation q.
score =
wY
n=1
S(w n a n ) (5.1)
49

That is: if we know the word class for each word in our utterance and if we know the probability
of each word class being associated with a stressed (or unstressed) word then we canmultiply
all the probabilities together to give avalue we call the score (it is not actually necessary to use
probabilities, any measure of likelihood would be acceptable but using probabilities (in reality an
estimate of the probability) means that all the measures of likelihood will always be in the range
of 0.0 to 1.0 and hence one does not have toworry about overow errors when using very long
sequences. Underow errors can, of course, occur but this is easy to handle since any sequence
score that underows is going to be very unlikely and therefore will not be a candidate).
By summing frequencies for all stress marks in the tonic stress/word class co{occurrence table
for each word class the two values (for frequency of stressed and frequency of unstressed) can
be used to calculate probabilities of each word class being stressed or unstressed. For NP1 (see
table D.3) we get the frequency of NP1 being unstressed at 117 and the frequency of NP1 being
stressed at
91 + 52 + 129 + 191 + 131 + 191 + 37 + 155 + 222 = 1199
The probability of NP1 being unstressed is then
117=(117 + 1199) = 0:09
and the probability of NP1 being stressed is
1199=(117 + 1199) = 0:91
Given that the word class is known for each word in the utterance 2
wecannow state the
likelihood of stress being present on each word. As an example consider:
2 It is assumed that this information may be derrived automatically using a tagging system such as CLAWS).
50

Word at Ford motors
Word Class II NP1 NN2
Probability of being stressed 0.12 0.91 0.92
Probability of being unstressed 0.88 0.09 0.08
Which means, for example, that the word class NP1 (singular proper noun) has a 91% chance
of being stressed and a 9% chance of being unstressed.
Then for each of the possible stress
sequences the scoring would be as follows (S represents stressed annotation, U represents unstressed
annotation so USS means that \at" will be unstressed whilst both \Ford" and \motors" will be
stressed):
pattern calculation score
UUU 0:88 0:09 0:08 = 0:006
UUS 0:88 0:09 0:92 = 0:073
USU 0:88 0:91 0:08 = 0:064
USS 0:88 0:91 0:92 = 0:737
SUU 0:12 0:09 0:08 = 0:001
SUS 0:12 0:09 0:92 = 0:010
SSU 0:12 0:91 0:08 = 0:009
SSS 0:12 0:91 0:92 = 0:101
Using this simple scoring scheme reasonable results are achieved (in the order of half of the
predictions matching the corpus annotation). In this case \at Ford motors" is the winning
sequence by a long way however the next best sequences \ at Ford motors", \at Ford motors"
and \at Ford motors" are all plausible.
5.2.3 Performance Measures
An important consideration is now evident: how does one rate the performance of the annotations
That is: how does this generated annotation compare with real speech. This is a dicult question
but using the corpus as a guide two possibilities are:
51

calculate the percentage of words with the annotation marks which are the same as those
annotated in the corpus.
calculate the number of (tone unit) sequences that are entirely the same as those annotated
in the corpus.
The latter performance measure was used initially since synthesis worked on a sequence of several
words at once. One error in a tone unit might upset a listeners perception of normalness. However
this performance measure proved to be too inexible to register minor improvements in the performance
(the main reason for use of such a measure is to assess the function of the model). Indeed
the majority of errors that indicated poor performance were likely to be unpredictable without
additional information such as semantics (afterall one would not expect 100% performance from
this type of model). Such errorsovershadowed those errors that could have been improved upon
within the constraints of the model. It was also discovered that sequences that contained errors
were only wrong in one place 69% of the time. The change to the former performance measure
brought understandably higher percentage gures but most importantly provided better insights
into how changes eected the synthesis results. Neither of these are wholly satisfactory because
they do not take account of the fact that alternative annotations to those given in the corpus may
be acceptible. The matter will be returned to later.
5.2.4 Context
Something to notice about this simple scheme is that it takes no account oftheorderofthe
words.
Hence \at Ford motors" (II NP1 NN2) and \Police in Yorkshire" (NN2 II NP1) will
attain the same scores regardless of the word order and regardless of whether the change in order
dramatically changes the way that each word would be stressed (it is unlikely however in this
case). There is strong reason to suggest that the order of stress annotations is important asis
noted in section 4.2.3 there is a tendency for a TSM to come at the end of a tone unit and it
is relatively likely for an unstressed word to appear at the start of a tone unit. A renement to
formula 5.1 can take account of the sequence order. To calculate this we can use the probability
52

of a stress occuring at each word and the probability of the stress sequence. Ideally a value is
needed that represents the relative likelihood (or probability) of each sequence of word classes
and stress annotations. However these values are dicult to extract from the corpus because the
corpus is not large enough to provide enough examples of each word class sequence in dierent
stress patterns to get reliable probability orlikelihood measures.
Although it is not possible to extract likelihood measures for any arbitary sequence of word
classes it is possible to approximate this by using likelihoods for shorter sequences and overlapping
them. Making use of xed length short sequences also considerably simplies calculation of the
score.
For example the sequence II NP1 NN2 could be divided into the two sequences II NP1 and
NP1 NN2. The likelihood for II NP1 NN2 can be estimated as the product of the likelihoods for
the sequences II NP1 and NP1 NN2. Two{symbol sequences like this are known as bigrams 3 .
The new scoring metric given in equation 5.2 includes components for both the likelihood of
stresses occurring with word classes and the transition likelihoods for bigram sequences of word
classes with specied stress annotations.
score =
wY
S(w n a n )
wY
n=1
m=2
B(w m;1 a m;1 w m a m ) (5.2)
Where S(p q), w n ,anda n are as before and B(p q p 0 q 0 ) is the probability (or likelihood) of
the bigram of word class p followed by word class p 0 where p has an annotation q and p 0 has an
annotation q 0 .
Bigrams Probabilities
For the bigram probabilities four tables are needed, one for each of the possible stress transitions:
3 A bigram is a sequence of two symbols, such astwo word class tags that follow each other in a text. Trigrams
or more generally n{grams are three or n symbol sequences.
53

unstressed ! unstressed
unstressed ! stressed
stressed ! unstressed
stressed ! stressed
Each table has to hold the likelihoods for transition from any of the 168 word classes to any other
word class.
Four tables of 168 168 cell locations still need very many more samples than are available in
the corpus size of appoximately 28000 words 4 to produce reasonable likelihood measures. It is clear
that even these values cannot be extracted. One (possibly extreme) solution to this problem (but
see section 5.4) is to assume that all word classes behave similarly in these bigram probabilities.
In all likelihood many word classes will behave similarly and their likelihoods could be combined
to give groups of word classes that occur similarly with regard to stress pattern/word class orders.
The extreme case of assuming that all word classes behave similarly (which isequivalent toa
single group) will mean that only four values (one per table) need to be estimated. In fact in this
approach word classes are ignored. This is a simplication to B(p q p 0 q 0 ) such thatp and p 0 are
irrelevant and are ignored or more generally all word classes are mapped to the same group. The
grouping idea will be returned to later. These four probabilities can be calculated from the corpus
prosodic annotations and are shown in the table 5.1.
first nsecond unstressed stressed
unstressed 0.17 0.39
stressed 0.21 0.23
Table 5.1: Stress Transition Table.
This means that for the sequence where the rst word in unstressed and the second word is
stressed (in a bigram) the probability is0:39.
4 Section 4.1 lists the categories if the corpus used for this research. Note that some sections of these categories
are also omitted because of problems between the various corpus versions.
54

For a given stress pattern the following probabilities can now be laid out (where arrows indicate
transition (bigram) probabilities and initial state probabilities (probabilties of word classes being
stressed or unstressed are listed below eachword class). This example is for the specic stress
annotation \at Ford motors".
at Ford Motors
II NP1 NN2
0.39 0.23
0.88 0.91 0.92
prosody
word tags
transition probabilities
state probabilities
The product of all these probabilities is calculated (using equation 5.2) giving the overall
likelihood for this utterance. This is repeated for all possible patterns and the highest scoring
pattern is selected giving the \most likely" binary pattern.
Here are the new values for the example (note that although the order of most sequences is
the same UUS and USU are reversed):
pattern
score
UUU 1:7 10 ;4
UUS 4:8 10 ;3
USU 5:2 10 ;3
USS 6:6 10 ;2
SUU 3:6 10 ;5
SUS 8:2 10 ;4
SSU 4:3 10 ;4
SSS 5:3 10 ;3
5.2.5 Boundary Conditions
Until now the subject of boundary considerations has been ignored. It is possible to calculate
bigram frequencies from the corpus prosodic annotations for stress annotations after and before
prosodic boundaries (see tables 5.2 and 5.3) and incorporate these into the calculations. Assuming
that the sequences that are processed with the model are whole tone units (bounded on either
55

TU boundary
stressed 0.1781
unstressed 0.0139
Table 5.2: Probability of a tone unit boundary following a stressed or unstressed word.
stressed unstressed
TU boundary 0.0690 0.1231
Table 5.3: Probability of stressed or unstressed word following a Tone Unit boundary.
side by a tone unit boundary) the additional probabilities: tone unit boundary followed by either
stressed or unstress annotations (dependent upon the stress state of the rst word) and likewise
for the last word in the tone unit can be applied by multiplying them together with the value
given by formula 5.2.
For the example sequence \k at Ford motors k" the score given above (6:6 10 ;2 )would be
multiplied by 0.1231 for the \k at" bigram and by 0.1781 for the \ motors k" bigram.
This model performs no kind of boundary prediction. In the results presented below the original
boundaries were taken from the corpus prosodic annotations. Boundary conditions do eect the
results (there is a greater tendency for a stress at the end of a tone unit than at the beginning)
but the models (in this research) are not concerned with boundary predictions or modelling even
though their use here does improve the model's performance. Initially a distinction was made
between boundary types but this was later dropped and all boundary types are treated the same
because there was no signicant dierence attained by making the distinction.
5.3 Performance
To assess the accuracy of the model (see source code in section F.9) it was applied to the training
data in the corpus 5
and the predicted stress patterns compared with those transcribed by the
5 It is usual to use testing data to assess a model's usefulness but this is not the purpose here. Here we assess
how well the model matches the training data. Testing data would then be used to compare with the training
data results to see if the model worked as well for unseen data Category M was reserved for checking the models
generality and results for this are presented in chapter 6.
56

Category Speech Style %BJW %GOK %ALL
A Commentary 88 94 91
B News Broadcasts 90 95 92
C Lecture(general) 88 94 90
D Lecture(specialist) 92 95 93
F Magazine Reporting 85 94 90
Table 5.4: Performance statistics for stress prediction model. Percentage of words which are
correctly stressed/unstressed in comparison to the two expert annotations and overall.
Category Speech Style %BJW %GOK %ALL
A Commentary 50 74 64
B News Broadcasts 54 82 69
C Lecture(general) 55 75 66
D Lecture(specialist) 66 79 73
F Magazine Reporting 43 76 64
Table 5.5: Performance statistics for stress prediction model. Percentage of completely correct
tone units in comparison to the two expert annotations (BJW: Briony Williams, and GOK: Gerry
Knowles) and overall (ALL).
experts. The results are summarized in table 5.4. The performance of the model is good averaging
over 90% agreement with the annotations in the corpus for a range of speech styles however it seems
especially good with the more formal speech styles in the specialist lecture and news categories.
It is interesting to note that using the second performance measure listed earlier the results
in table 5.5 were attained. The interesting point to note is not that the values are lower (as this
would be expected) but that the values for the dierent transcribers are signicantly dierent.
The values in the last three columns show the percentages of correct predictions for tone
units (average length 4 words) in each catagory. The percentage correct is the percentage of tone
units whose predicted stress pattern completely agrees with that in the transcription. There is an
average 24% dierence between the accuracy of the model when applied to each of the transcribers'
sections of the corpus. This does not mean that one transcriber was better or worse than the other
as both transcribers are experts in the eld and were approaching the same task. However it does
go some way to illuminate the diculty in producing prosodic transcriptions. Prosodic factors are
complex perceptions and a person's perceptions are avoured by interests, attitudes, environment
etc. It is reasonable to expect a level of \perceptual variability" between any two transcriptions.
57

It is not suprising, therefore, that one transcriber had a tendency to produce transcriptions more
consistent with syntactic structure (he having been working in that area for many years) whilst
the other transcriber produced transcriptions more consistent with acoustic measures of speech
(she working on speech and intonation synthesis at that time).
Since the model is based on
syntactic class it is clear why the percentage gures for GOKs transcribed sections were larger.
This dierence is amplied by the metric used.
5.4 Improvements
As suggested earlier clustering word classes into groups with similar probabilities will enable us
to estimate word classs/stress state transition probabilities for groups of word classes and thus
side{step the problem of low sample sizes. If the assumption made above that word class is not
important for these probabilities is wrong the estimated group{based probabilities will perform
better than the non{group{based probabilities used before. In which way the groups are formed
is the subject of the following.
There are potentially many ways to group word classes. Initially they were grouped upon the
ratio of frequencies of stressed to unstressed instances of each word class. That is the probability
of each word class being stressed and unstressed were used to plot a point on a graph. All points
lie on the line
y =1; x where 0 x 1
An arbitrary thirteen groups were produced by dividing this line at arbitrary points and the
group stress transition probabilities were extracted for each group from the aligned prosodic and
syntactic annotations (this produced four tables of 13 13 = 676 cells). This was achieved by
summing the transition frequencies for each word class and then adding together frequencies for
each word class in each group and translating between word classes and groups. These transition
probabilities were then used in place of the probabilities in the stress transition table. Each word
class is mapped onto its appropriate group and the group{to{group transition probabilities used.
58

Initial word class state probabilities were used as before as opposed to calculating group state
probabilities.
The probabilities made no new mistakes and when tested upon a sample of 50 previously erroneously
predicted sequences 16% were corrected. This translates to an overall 3%{4% improvement
thus demonstrating that grouping of word classes to aid estimation of transition probabilities improved
the model. However, the expected improvements were not forthcoming when the testing
was scaled up.
This failure prompted a closer examination of the groupings. There is a problem with grouping
word classes in this way consider a very low frequency word class (for example NNSA1 which has
one instance in the sub{corpus sample used). A word class with only one instance will be placed
at either end of the continuum stressed/unstressed and no account is taken of the behaviour of
other word classes that might be similar. This is true for other low frequency word classes: it is
quite possible for a single extra example of the word class to shift which group it would be placed
in.
A dierent grouping scheme could be to use transitions probabilities themselves as a guide
to group formation but this suers from all the problems mentioned earlier and is therefore not
practical. A more realistic scheme would be to use the word class/prosodic mark co{occurrence
frequencies as a guide see section 4.3.3.
To avoid the problem of low frequency word classes aecting the groupings the clustering
was performed on the 64 most frequent word classes. This was an arbitrary cut o point. The
remaining word classes were then placed into groups using the similarity ofword classes already
in the groups as a guide and was performed some what ad hoc. The clustering into groups was
performed using the method described in Hughes[Hug94] where the distance between two word
classes as dened by the distance between the vectors of prosodic mark co-occurrence gures for
each word class was calculated and closest word classes were merged. The vector for each word
class contained the probabilties (i.e. normalised frequencies) for co{occurrence with unstressed
words, levelly stressed words, stressed but unaccented words and words with stress accent.
59

1 2 3 4 5 6 7 8 9
&FW EX AT CCB DA &FO NNS ND1 DD1
APP$ IF AT1 CF DA1 DD NNS1 NN1 DD121
CC PNQO BTO CS DA2 MC NNS2 NN121 DD122
CC31 PNQS BTO21 CS21 DA2R MC{MC VVD NN122 DD2
CC32 PPH1 BTO22 CS22 DAR MC1 VVG NN2 DD21
CC33 PPIO1 CSN CSA DAT MC2 VVZ NNJ DD22
II PPIO2 CST CSW DB MD NNJ1 DD221
IW PPIS1 II22 DDQ DB2 XX NNJ2 DD222
PN PPIS2 II31 DDQ$ JA NNL1 II21
PN1 VB0 II33 DDQV JB NNL2 II32
PN121 VBDZ IO ICS JBR NNT1 MF
PN122 VBN PPY LE JJ NNT2 NNO
PP$ TO NNSA1 JJR PPX1 NNO2
PPHO1 NNSB1 JJT PPX121 NNU
PPHS1 PPHO2 NN PPX122 NNU1
UH PPHS2 NP PPX2 NNU2
VBDR VBR NP1 PPX221 NNU21
VBG VH0 NP2 PPX222 NNU22
VBM VM RA NPD1
VBZ VM21 RL NPM1
VD0 VM22 RL21 REX
VDD VMK RL22 REX21
VDG RR REX22
VDN RR21 RG
VDZ RR22 RG21
VHD RR31 RG22
VHG RR32 RGA
VHN RR33 RGQ
VHZ RRQ RGQV
RRQV
RGR
RRR
RGT
RRT
RP
RT
VV0
VVN
ZZ1
Table 5.6: Words classes in the groups.
This is repeated until all word classes were merged together. From the resulting distances a
dendritic diagram could be produced (see gure 4.3). By cutting vertically through the lines of
the dendritic diagram the word classes may be divided intoanumber of groups. This was done
to yield nine groups.
The word classes in each group are presented in table 5.6.
After all word classes have been placed into a group it is possible to calculate the transition
probabilities.
This was performed using the two programs transition and transgroups see sec-
60

Category Speech Style %Correct
A Commentary 92
B News Broadcasts 93
C Lecture(general) 92
D Lecture(specialist) 93
F Magazine Reporting 93
Table 5.7: Performance statistics for stress prediction model using group transition probabilities.
tions F.6 and F.7 respectively. The rst program calculates the absolute transition frequencies for
each word class (that is the number of times each word class/prosodic mark pair is followed by
another word class/prosodic mark pair) and the second maps the word classes (and their associated
frequencies) into groups before normalising the frequencies into the range 0 to 1 giving the
estimate of probability.
As before only two stress states are of concern here: stressed and unstressed, however, transitions
to and from tone unit boundaries are also calculated, so a special group is assigned to the role
of representing tone unit boundaries although it is clearly not possible for tone unit boundaries
to be stressed or unstressed. For convenience and simplicity they are always treated as unstressed
by convention, even though this has no meaning.
This produced results (see table 5.7) equivalent to those shown in table 5.4. This demonstrates
that clustering can produce results at least as good as those produced earlier, in this case slightly
higher.
5.5 Summary
With performances as high as 95% for some categories (see table 5.4 categories B and D) it is obvious
that the model performs very well. A performance of 100% correct would be remarkable and
it is clear that the models' performance is approaching a limit in terms of possible improvements
using just word class and bigram frequencies.
It is highly likely (but outside the scope of this work) that contextual information (such as
given information) could improve performance.
The results are encouraging enough to try to
61

expand the model to predict more than two types of prosodic mark. That is to expand the model
such that it produces a potential prosodic annotation for a sequence of word classes. In addition
this will quantify the relationship between word class and stress accents. This is the subject of
the next chapter.
62

Chapter 6
Automatic Prosodic Annotation
6.1 Introduction
Chapter 5 described a model for predicting stress patterns for word class tagged text. This chapter
concentrates upon extending the model developed to make predictions about stress accents 1 .
The basic function of the model is as described in the previous chapter hence for a background
description of the mathematics and functioning of the model refer to chapter 5.
6.2 Expanding the Model
The stress prediction model (SPM) developed in Chapter 5 showed that there was a good degree of
relationship between word class and stress such that for over 91% of word classes the stressed versus
unstressed state of the word may be predicted. That is a probabilistic model has been derived
which can generate acceptable levels of stress annotation from word class sequences. This shows
that stress is closely related to word class. This has been a widely held belief but has not until
now been quantied in such away due to the unavailability of hand annotated machine{readable
corpora such as the SEC.
1 Whether a stress has a rising or falling or level pitch
63

This chapter extends the model to show that this relationship holds too for stress accents (to a
lesser extent). That is: it shows that there is a relationship between word class and pitch accents
and that to a certain degree stress accents can (reasonably accurately) be generated from word
classes.
Three points however need to be made. Firstly the degree to which it is reasonable to expect
such a model to function accurately will be less than for the previous model. This is partly due
to the increased diversity of the possible annotations and partly due to the fact that the stress
prediction model is a more general case than that of stress accent prediction so in predicting
accents we are rening the model's behaviour.
Secondly, as has repeatedly been pointed out, it is unreasonable to attempt to deduce an
accurate statistically based model from a corpus as small as the SEC. That is not to say itis
impossible but any models will be subject to deciencies in the same areas where the corpus
suers from deciencies. For example the tonic stress mark for rise{fall is so infrequent in the
SEC as to make it impossible to model it statistically. The same is true for certain word classes e.g.
NP2 and REX. In general it is still possible to model a category (prosodic or syntactic) accurately
despite low frequency if and only if it is highly constrained. The few examples are enough to
denitively illustrate the category's behaviour.
Finally, stress seems to play an important role in giving information about utterance (or sentence)
structure whereas stress accents also play important roles in giving semantic and contextual
information. The eect of these will not be modellable within the constraints of this research.
For these reasons the model developed here does not attempt to reproduce the exact set of
annotations with which the corpus is annotated, but instead uses a subset as described and justied
in section 6.3.1 below. This brings up the issue of how to compare the two annotation schemes.
Section 6.4 describes an alternative metric 2 to that used previously (i.e. an exact match with the
corpus annotations) that attempts to overcome these problems.
There is, and continues to be, a real lack of ability to assess general performance of any such
2 None of the metrics used for assessment are wholey satisfactory.
64

model since it is beyond current capability to know what comprises a good prosodic annotation
for a given word class sequence | unless, of course, one is an expert in prosodic annotations 3 .
An expert transcriber or a linguist who had worked with prosody would have anintuitive feel for
what transcriptions were acceptible or natural. No system has yet been invented that can do this.
In addition no system has been invented that can generate speech with the appropriate stresses
and intonations from such transcriptions (thus performing the inverse role of the transcriber).
One possibility (See section 6.4) would be to synthesize utterances or manipulate pre{recorded
utterances with the desired changes in fundamental frequency, intensity, duration and other
prosodic features to reect the stress accent predictions and submit these to listening tests whereby
subjects assess the naturalness of each synthesized utterance. Attractive though this is it is not
possible to achieve at present because of lackofknowledge of how (and if) the prosodic annotations
relate to acoustic features such as fundamental frequency, intensity etc.
6.3 Model Design
The model used in this chapter works on the same principles as the stress prediction model but
with two important dierences. Firstly the range of annotation symbols is increased from stressed
and unstressed to incorporate rises and falls (see below) and transition probabilities are estimated
from groupings of word classes using their prosodic similarity (in the ultimately described model).
There are two basic details to consider in the design of the model and these are: what range
of word classes the model should handle and which aspects of the prosodic annotation will be
modelled. There are also two sets of parameters that need to be estimated from the corpus: the
probability of co{ocurrence between word classes and prosodic marks and the transition probabilities
of a pair of word classes with appropriate prosodic marks.
The choice of which word classes to model is largely dictated by those word classes that will
be in the input stream. However as pointed out above itwould be impossible to model some word
3 unlike the author
65

classes because of their low frequency. The action taken was to attempt to model all word classes
however erroneously that may be for some word classes. In attempting to model such word classes
the overall performance of the model will be reduced. It should be noted however, that by their
very nature these word classes hardly occur and so each word class badly modelled will not have
a signicant impact upon the performance.
A related factor to this becomes obvious when we want to assess whether changes to the model
are improvements or not. Since most errors of the sort caused by poor modelling occur in those
word classes with ill{dened probabilities (i.e. those with low frequencies) performance changes
are unlikely to be large and hence will be dicult to assess. It would be quite justiable to ignore
the annotations on some of the word classes output from the model (if they were in the poorly
dened class). This would leave the problem of side eects that happen over transitions between
these word classes and their neighbours. In order to not complicate the analyses more than is
necessary this is not done. Performance gures are given for the more frequently occuring word
classes. See section 7.3.3.
6.3.1 Choice of Prosodic Marks
The corpus has a wide range of prosodic marks:
and unstressed with the
" and # modier symbols. It is unreasonable to expect this model to perform well with so many
possibilities for reasons given above and the search space would become unreasonably large. This
must be reduced. Dropping the high/low distinction (as in Roach[Roa91]) and the rise{fall mark
( ) which isvery infrequent, reduces the set of marks to six high and low resets are generally
ignored in these models. The symbol (stressed but unaccented) has been described as ill{dened 4
and a decision was made to merge the low and high level tones ( and )with to give anew
mark (here it will continue to be donoted as
but its meaning is simply stress or level stress).
This results in 5 possible prosodic classes: rise, fall, fall{rise, stressed and unstressed. Unstressed
words will continue to carry no mark.
4 Gerry Knowles in private conversation.
66

For convienience we will continue to use the symbols and to represent these prosodic
marks but it must be remembered that these symbols do not now distinguish between high and
low levels of marks.
6.3.2 Estimation of Probabilities
Estimating a model of stresses or tonic stress marks statistically from a corpus would normally
require enough data for each entity to be modelled. As pointed out in section 5.2.4 the SEC is
not a large corpus and the direct methods of estimating co-occurrence probabilities are prone to
error. However in section 5.2.4 an assumption was made that all word classes behave similarly
with regard to stress transition likelihoods.
This is not a valid assumption (but was a useful
way to simplify the calculation of the likelihoods) because improved results may beachieved by
grouping word classes in terms of their behaviour and estimating likelihoods for each group (see
below on transition probabilities). If there is a sucient number of examples of the dierent word
classes in each group (which thus imposes some constraints upon how groups are comprised) the
likelihoods of co{occurrence between word classes (or more accurately, groups) and tonic stress
marks can be estimated with reasonable accuracy. Section 4.3.3 discusses grouping word classes
using the 64 most frequent as a guide. The remaining word classes which are of low frequency
can be inserted into groups based upon the similarity oftheword class types. It should be noted
that the clustering of word classes into groups performed in section 4.3.3 does so not based upon
the transition likelihoods but on the co-occurrence likelihoods, however, the enhancements to the
model provided here assume that word classes that have similar co-occurrence likelihoods will also
have similar transition likelihoods. This is described in more detail in section 5.4.
Estimation of State Probabilities
It would be possible to use the state probabilities for the group associated with each word class.
This would mean that low frequency word classes would use an \improved" set of probabilities
but at the expense of those word classes that are of high frequency. Since these latter word classes
67

are the most frequent it is desirable to have them as accurate as possible. The decision made here
is to use the state probabilities derived from the co{occurrence table directly individually for each
word class. An alternative would be to use the group state probabilities for low frequency word
classes only. The problem here is that it is still not clear which group a low frequency word class
should belong and consequently this approach has not been explored although there is reason to
suspect some improvement in performance.
Estimation of Transition Probabilities
The estimation of the transition probabilities can be attempted in three ways: rstly ignore word
class and use prosodic mark transition probabilities. This is equivalent to the special case of a
single word class group. Secondly the transition probabilities for each and every word class could
be estimated from alignment data produced in chapter 3. This is equivalent tohaving groups
with a single word class in each. Finally a compromise could be struck using a small number of
groups on the basis of similarity. Although there is good reason to expect that this compromise
would be the most benecial the construction of groups is still an uncertain process and has
signicant impact upon results as can be seen by the two approaches taken in section 5.4. The
estimation of probabilities is fraught with low sample size problems: each group must contain a
sucient number of word classes with a representative sample of transitions. For this reason the
transition probabilities are estimated on the single group basis which has been shown to work if
not optimally. It is also important to not over{complicate the model initially. In section 6.3.4 the
concept of a composite model is introduced. This model uses aspects of the model developed in
the previous chapter along with the model developed here. This, of course, leads to two lots of
transition probabilities. The transition probabilities for the rst stage of the composite model use
the groupings described in section 5.4 and the transition probabilties for the latter stage of the
composite model use the single group transition probabilities mentioned above.
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6.3.3 The Model
The formula for the model is very similar to that given in equation 5.2. There are however now
more annotation types: the ve marks given in section 6.3.1. As in the original stress prediction
model (SPM) transition probabilities are used with the assumption that word class is not important
(i.e. the extreme case of one group).
The source code for this model is given in section F.10. This program is very computationally
intensive because of the exhaustive search of the very large search space. In an attempt to limit
computation time the length of the utterances that it will handle is limited to 15 words (and/or
tone unit boundaries). In most situations however utterances (divided by tone unit boundaries)
will be much shorter than this. There is no special requirement that the utterance is bounded by
tone unit boundaries and it is worth noting that this will aect performance since the presence
of a tone unit boundary will aect the placement of tones given the inclusion of TSM|tone unit
boundary bigram likelihood constraints (see section 5.2.5). In particular it may be found that
TSMs will not be placed at the end of what would be a tone unit if a tone unit boundary is
omitted.
Boundary Considerations
In a real life application of the model it would be necessary to predict the location of the tone unit
boundaries. This is beyond the scope of this research but cannot be entirely ignored. The model
makes no serious attempt to model tone unit boundaries other than assuming that punctuation
gives rise to a boundary. As punctuation marks are classed as lexical items and assigned their own
syntactic word tags in the SEC (and most other tagged corpora) this amounts to an appropriate
mapping between syntax and prosody at a basic level. This is a very rough and ready rule which
aects the performance of the model but is only described as a short term solution since much
work has being done in this area.
Table E.1 quanties the relationship between punctuation and tone unit boundaries. It should
be noted that this rule will miss approximately 52% of the boundaries and approximately 9%
69

of the boundaries generated will be inserted where they would otherwise not have existed.
It
is undoubtable that this would aect the accuracy of the model in some cases.
In the results
presented here and elsewhere in this thesis the original tone unit boundaries (as transcribed in the
corpus) were used for the assessment of the model. Punctuation would only be used in situations
where tone unit boundaries were not available.
6.3.4 Composite Model
The major failing of the model above is that it performs badly on the stress/unstress distinction.
The stress prediction model however achieves a high success rate in this very area (over 91% on
average). It is therefore desirable to try to capitalise on this.
A composite model (i.e.
a model that combines both the model described above and the
stress prediction model) may be devised by making use of the mechanism of the model developed
in chapter 5 to select a number of candidate sequences and use these as input to the prosody
predition model. In this approach the stress prediction model is applied to utterances and the
top few sequences are selected. Analysis of the models' performance has shown that the \correct"
or most acceptable stress pattern was usually present in the top 5 patterns. The prosodic mark
prediction model now operates on the search space dened by the top few patterns: the stressed
words will be allowed to vary between each of the accents and and the unstressed words
will be clamped as unstressed.
For example the utterance \at Ford motors" as processed by the SPM gives performance scores
as listed in section 5.2.4 with the winning sequence being \at Ford motors". This sequence would
be constrained to the possibilities listed below. Remember that
has dierent meanings in the
SPM and PPM.
70

at Ford motors
In this case the winning sequence was \at Ford motors" which is considered to be equivalent
to that annotated in the corpus of \j at
Ford motors j".
Of course the SPM is not perfect and its errors will be passed on to the second phase of the
model. A better solution would be more accurate estimates of the model parameters: state and
transition probabilities as mentioned elsewhere.
Alternative search methods such assimulated annealing are not guaranteed to nd the global
minimum whereas the above mechanism is highly unlikely to miss the global minimumand achieves
avery large reduction in the search space it is therefore reasonably hard to beat. There seems
little justication in attempting to implement alternative search methodologies.
71

Category Speech Style %Correct
A Commentary 64
B News Broadcasts 77
C Lecture(general) 59
D Lecture(specialist) 63
F Magazine Reporting 61
Table 6.1: Performance scores for the training categories.
Search Space Reduction
The non{composite model described above suers from a high computational load which makes
it unsuitable for real time applications: the search space is too large to search in reasonable time
given current computing power. Although the global search mechanism could be replaced with
an alternative there is no need: the composite model described in section 6.3.4 massively reduced
the computational load.
For example: a ten word utterance would have a search space of size 5 10 patterns as compared
to the composite model which would have a search space of the order 2 10 + 100 (with the, very
reasonable, assumption that the composite model selects 5 patterns from the rst phase to pass
to the second phase and if we assume that, on average, half of the words in the utterance will be
unstressed (5 words 4 prosodic marks 5 patterns = 100). We note that 2 10 + 100 5 10 .
As noted in section 4.2.2 and by reference to gure 4.2 the average length of a tone unit is
about 4 words. This would mean a search space of less than 130 patterns which iseasilyachievable
in real time. On a computer capable of 1Mop (and performing no other work) it could achieve
in the order of 1000 tone units per second equivalent to more than 4000 words. For an assumed
speech rate of ve words per second this would account for approximately 1/8% of the computing
power.
Performance Improvements
The performance statistics for the training categories of the model are presented in table 6.1.
Results for the testing sections are given in table 6.3.
72

There are a whole range of alternatives that may be used to attempt to improve the performance
of the model. One subset of these alternatives changes the transition probabilities. In addition
to the bigrams it is possible to calculate tri{gram probabilities for prosodic marks (i.e.
these
probabilities are for prosodic marks only not for word class and prosodic marks as it has previously
been noted that probabilities such as those cannot reliably be estimated from the corpus). The
addition of these tri{gram probabilities does provide a small increase (overall performance increase
of 0.18%) in the performance of the model overall, but not really of any signicance.
Varying the number of sequences passed to the second phase from the rst phase does not
make any dierence to the performance of the model. It would be possible to dynamically vary
the number of sequences passed on based upon the closeness of the scores for each sequence in the
rst phase. This could further reduce the search space. The top two or three sequences are the
most that is required.
Running the model with either the initial state probabilities or the transition probabilities
alone reduces its performance to just over half what is achieved when using both.
6.4 Model Assessment
The stress prediction model developed in chapter 5 could predict one of two possible prosodic
marks for each word class. The metric used to assess how well the model worked was to count the
number of times that each predicted mark matched the actual mark (or equivalent actual mark)
in the corpus. This was quite acceptible. In the case of the model developed in this chapter the
situtation is more complex since the model can predict multiple types of mark. Using the above
metric would disadvantage the model since it is clear that an error of one type maybeworse than
an error of another type. In fact what may be construed as an error (in that it does not match
exactly the annotation in the corpus) may be an acceptable prediction (in that a listener would
not object to the naturalness of the utterance).
In an attempt to alleviate this problem each
predicted mark is scored (ranging from 0 to 1) depending upon its similarity to the annotation
73

given in the corpus as opposed to the score being 1 for an exact match and 0 for a mismatch. For
example if the corpus annotation was a fall{rise and the model predicted a fall this would be given
a higher score than if the model predicted an unstressed word. The scores were given according to
the gures in table 6.2. For example: a predicted mark of
would score 0.15 if the corpus word
were annotated with
but would score 0.0 if the corpus word were annotated as unstressed. In all
cases a score of 1.0 is given where an exact match isachieved. The nal score for a section can be
converted to a percentage by dividing the score by the numberofwords.
It should be acknowledged that these scores have been allocated somewhat arbitrarily although
it would be possible to choose the scores so that the models behaviour seemed to be improved.
This has not been attempted and in the example performance statistics given in table 6.3 the
percentage correct metric is also given for comparison.
Scoring or evaluation is a notoriously
dicult problem in natural language processing in general.
For example see [Wee94, Lyo94,
BGL93] for discussions of the range of dierent and contradictory metrics of parsing sucess used
in corpus{based grammatical analysis systems.
As will be noted when viewing the performance statistics in table 6.3 the percentage scores are
higher than the percentage correct. This is to be expected given the nature of the way the scores
are calculated. Although the gures seem similar it should be realised that the percentage scores
aim to give a better idea of how good annotations are and should be more sensitive tochanges in
the model than the percentage correct. They have only really been useful when comparing diering
versions of the PPM. A more thoughtful and insightful approach to assignment of the values in
table 6.2 would perhaps provide more sensitivity and improvement in annotation assessment but
this requires expertise in the area of prosodic tone labelling of speech.
The only really satisfactory way to assess the \goodness" of prosodic annotations is to listen
to speech spoken in a way to reect the annotations. A listening test experiment would present
subjects with a variety of dierent utterances with diering prosody and the subjects would give
a subjective opinion of the \naturalness" of each utterance. The only diculty with this approach
is the diculty in producing the utterances. Three possibilities are given below.
74

u/stress
1.00 0.25 0.50 0.50 0.15
0.25 1.00 0.75 0.50 0.15
0.50 0.50 1.00 0.50 0.15
0.15 0.15 0.15 1.00 0.25
u/stress 0.00 0.00 0.00 0.15 1.00
Table 6.2: Scoring relationship between predicted and annotated prosodic marks.
1. Speak the utterances following the prosodic annotations. This is not an easy task and
requires expert ability not readily available.
2. Modify the original corpus recordings (available on the MARSEC CDROM) using either the
SOLA/PSOLA 5 algorithms or by using re{synthesis techniques. This requires the specication
of intensity, fundamental frequency, and duration contours derived from the prosodic
annotation. It is not clear how to do this or indeed whether there really is a relationship
between the two which can be captured in an accurate computational model.
3. Finally, a speech synthesizer could be used which allows specication of syllable durations,
rising and falling tones and relative syllable loudness. There are, however, no such synthesizers
readily available as most that employany form of intonation control tend to be restricted
to either a falling or rising tune over each utterance. Listeners may deem the output \unnatural"
even if the predicted annotations are an exact match for MARSEC markup because
of poor synthesis rather than poor prosody prediction.
The Klatt synthesizer allows the specication of all the necessary parameters but this requires
that prosodic annotations are converted into fundamental frequency and intensity contours
and syllable durations which leads to the same problems as above.
The use of a listening test has distinct advantages: it gets away from the use of corpus{based
scoring metrics whichhave been criticised[Wee94]. It also allows for the fact that some annotations
may be acceptible in diering ways from those in the corpus and there may well be reason why
5 These two algorithms are digital signal processing techniques that allow the duration of a sound to be lengthened
or shortened without changing the pitch (SOLA) and allow the pitch of a sound to be changed without changing
the length of a sound (PSOLA).
75

the corpus annotation is not typical or would not be acceptible in a general case. Context may
have forced a change in the prosody in the corpus which would not ordinarily have happened. For
example where the speaker wishes to contrast or correct something that the listener has misheard.
For example \Peter isn't here" versus \Peter isn't here".
6.4.1 Performance Statistics
The model performs quite well especially when it is realised that chance performance would be
20% (that is each output symbol may be one of ve possibilities) compared with that of the stress
prediction model where chance would be 50%. Overal score percentage is 1621:25(1420+763)
100 = 74:27% and the overall percentage correct is 1420 (1420 + 763) 100 = 65:05%. Here
score percentage is calculated as scoreright+wrong. See table 6.3.
The majority of test sections have good results over 60% correct. It is worth noting the the
best performance of nearly 74% correct is achieved on the longest section (m05). The sections (in
decreasing order of performance) cover the following topics.
m05
m04
m09
m08
m03
m02
m07
Nelson Mandela speech
Programme News
Programme News
Weather Forecast
Weather Forecast
Motoring News
Travel Roundup
It is clear that the model performs better for some types of speech and perhaps speaker. Above
the formal speech given about Nelson Madela and programme and weather news are given by
professional speakers who are more likely to speak with more accurate structure, pronunciation
and intonation than would be expected in casual speech.
76

Section # right # wrong Score Score % Correct %
m02 113 88 133.45 66.39 56.22
m03 86 57 102.70 71.82 60.14
m04 196 109 225.45 73.92 64.26
m05 548 196 597.65 80.33 73.66
m07 103 88 127.20 66.60 53.93
m08 90 59 108.05 72.52 60.40
m09 284 166 326.75 72.61 63.11
total 1420 763 1621.25 74.27 65.05
6.5 Summary
Table 6.3: Performance scores for the test category of the corpus.
In this chapter the stress prediction model developed in chapter 5 has been expanded to make
predictions for a range of prosodic stress marks. It was found that a model such asthishadavery
high computational load and was not very sucessful. The main problem area for the model seemed
to be in the stress/unstress distinction. However, the stress prediction model does this task well
and so a composite model was created. The composite model uses the stress prediction model
to select a number of stress patterns for the utterance from the search space and passes these
few patterns onto the prosody prediction model. This clamps unstressed words as such and only
allows stressed words to vary between the dierent stress accents. It was found that this produced
signicant improvement in performance with, on average, 65% of all prosodic marks matching the
prosodic mark in the corpus. This result was also seen in the testing data presented in table 6.1.
Chapter 7 goes on to analyse the function of the model in terms of how well it performs for
each prosodic mark and for each word class.
77

Chapter 7
Conclusions and Future Work
7.1 Introduction
This chapter rst presents a review of the main points covered in this thesis. It then covers analysis
of the models' performance in terms of how well it models each word class and prosodic mark.
Finally comments upon future investigations are given.
7.2 Review
In chapter 3 a need to be able to cross reference between prosodic and word class annotated
versions of the Spoken English Corpus was identied and a semi{automatic semi{intelligent tool
was devised to enable this. This tool produced an aligned version of the corpus with both word
class and prosodic annotations 1 from which itwas possible to extract statistics of co{occurrence
between the two as described in chapter 4. This tool is general enough to be exploited in a similar
way to relate any other types of corpus annotation and it has been used to provide alignment
information in the Machine Readable Spoken English Corpus[GAR92].
In chapters 5 and 6 two modelswere developed using statistics extracted from the corpus.
1 Treebank parse trees are also aligned, even though these were not used in experiments.
78

The development of these models was designed to demonstrate in a quantiable way the extent of
the relationship between the prosodic annotations and the word class tags in the Spoken English
Corpus. The models use two sets of parameters calculated from the cross reference between the
prosodic and word class versions of the corpus. These are the co{occurrence likelihoods (or the
likelihood that a prosodic mark occurs upon a word with a given word class) and the bigram
likelihoods (or the likelihood that a prosodic mark/word class combination is followed by another
specic prosodic mark/word class combination).
As far as has been possible within the limitations of the size of the corpus these likelihoods
have been estimated, although various alternatives have been considered to cope with problems
of low frequency of some entities. The most important of these techniques is the clustering of
word classes into groups on a prosodic basis. This has given rise to the concept of prosodically
orientated word class groups. It has been shown that these groups can perform as well, if not
better, in estimating bigram likelihoods.
The stress prediction model and the prosody prediction model have been tested with new
unseen text and both have demonstrated that they can perform good levels of annotations which
correspond 91% and 65% of the time respectively with the annotations within the corpus. These
models may be used as a stage in a text{to{speech system for the low level \baseline" assignment
of stress and prosody annotations. Higher level processes could use these as a starting point for the
assigment ofcontext and semantic dependent prosody before generating the prosodic annotations
acoustically.
7.3 Performance Measures
In order to evaluate the models in more detail it is necessary to look at performance gures for
individual elements within the model. This section presents and comments on several such elements
including the frequency of prosodic marks within tone units, the performance of individual word
classes and the dierences between predicted and actual prosodic marks. The latter two showin
79

which areas the models performance varies. These show inaquantitative way the relationship
between word class and stress accents.
7.3.1 Tone Unit lengths in the Model.
Although the model makes no attempt to generate tone unit boundaries (other than assuming
that punctuation indicates a boundary) the number of tonic stresses and stressed words in tone
units will dier between the model and the transcribed corpus. If the same tone unit boundaries
are used to segment the synthesized prosody we can compare the graphs shown in gure 4.2 with
the equivalent graphs in gure 7.1. It should be noted that the boundaries used are not exactly
the same since boundaries inserted due to punctuation have not been removed. This accounts for
approximately 330 extra tone unit boundaries. It is also important to realise that the model would
probably behave dierently if these boundaries had not been present in its input or if the tone
unit boundaries inserted to allow this comparison had been present in its input. This accounts for
the spreading to the right for the words curve. This does not have signicant impact upon the
other two curves.
Note that the model predicts a large number of tone units with 0, 1 or 2 stress accents (TSMs)
in comparison with the original data which has large numbers of tone units with 1, 2 or 3 stress
accents. This indicates that the model underpredicts many of the stress accents, although it is
not possible to tell what percentage of stress accents are present only as a function of context and
semantics.
7.3.2 Analysis of Models
There is a real need to be able to assess the \goodness" of stress patterns. Clearly there is a
dierence between the experts' transcriptions, but to what extent do errors go towards making
an utterance unintelligible Is it more wrong to miss a stress, insert one or transpose it It is
invisaged that this question could be answered by listening tests as described in section 6.4.
80

Tag SPM% PPM%
APP$ 91.46 89.76
AT 97.65 95.79
AT1 98.86 97.14
CC 93.09 86.68
CCB 93.71 81.44
CS 69.80 57.96
CSA 79.78 75.27
CST 99.24 96.99
DB 91.57 44.32
DD 76.29 45.36
DD1 65.06 36.04
DD2 56.76 36.00
DDQ 72.73 66.46
EX 96.51 89.77
ICS 58.77 53.33
IF 98.81 94.38
II 90.25 87.04
II21 61.76 40.85
II22 98.53 98.59
IO 99.88 98.87
IW 88.89 84.83
JB 88.64 48.66
JJ 92.30 52.43
JJT 87.76 31.37
MC 89.79 52.34
MC1 81.25 41.84
MD 80.99 41.40
MF 61.84 40.79
NN 85.03 42.44
NN1 92.88 35.96
NN2 90.97 35.29
NNJ 85.71 36.43
NNL1 85.61 40.56
NNO 66.22 45.33
NNS1 86.02 54.84
Tag SPM% PPM%
NNSB1 78.48 69.62
NNT1 82.41 35.07
NNT2 89.41 36.96
NP1 91.99 43.82
PPH1 99.19 93.13
PPHS1 92.06 87.69
PPHS2 82.73 80.67
PPIS1 86.21 77.97
PPIS2 95.15 90.57
PPY 97.14 95.77
RG 64.15 49.15
RL 88.29 38.60
RP 67.53 35.61
RR 86.00 42.49
RR21 94.44 80.00
RR22 92.59 33.33
RRQ 54.41 50.00
RT 79.59 42.59
TO 100.00 99.27
VB0 92.22 91.62
VBDR 89.80 89.90
VBDZ 94.66 92.88
VBN 91.26 91.23
VBR 87.97 85.44
VBZ 91.83 90.15
VH0 84.80 83.15
VHD 89.89 87.23
VHZ 89.83 88.43
VM 76.99 75.82
VV0 82.64 47.09
VVD 87.59 55.79
VVG 89.76 54.74
VVN 91.82 48.57
VVZ 78.65 51.55
XX 45.51 27.61
Table 7.1: Word class tags with frequencies of 50 or greater showing percentage of correct predictions
(when compared with the corpus annotations) for the stress prediction model (SPM) and
the prosodic mark prediction model (PPM).
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Frequency
3300
3000
2700
2400
2100
1800
1500
1200
900
600
300
0
0 1 2 3 4 5 6 7 8 9 10
Length of Tone-Unit
Figure 7.1: Relative frequencies of tone-unit lengths produced by the model in terms of numbers
of: words with tonic stress marks words with prosodic marks and words.
7.3.3 Word Class Models
The gures in table 7.1 show for both models how well the predictions match the corpus for each
word class. Word classes with frequencies of less than fty have not been presented because they
are poorly modelled due to insucient data they are not of primary concern to the model since
their low frequency means that they have alow impact upon the models.
With some exceptions the PPM performs just as well as the SPM in all word classes except
for determiners (DB{DDQ), adjectives (JB{JJ), numbers (MC{MF), nouns (NN{NP1), adverbs
(RG{RT), lexical verbs (VV0{VVZ) and not or n't (XX). The performance of XX is probably
poor due to its frequent inclusion in enclitics. It is no great surprise to discover that those word
classes upon which both models perform well are those that are mostly unstressed. Remember
for the SPM a chance result would be 50% correct but for the PPM a chance result would be
20%. For the PPM most predictions are between 15% and 75% higher than chance. However high
performance was achieved for (APP$), articles (AT{AT1), conjunctions (CC{CST), existential
there (EX), prepositions (ICS{IW), it, he/she, they, I, we, you (PPH1{PPY), innitive marker to
82

U/stress
48.86 2.87 1.03 38.88 8.36
66.46 2.14 0.22 27.26 3.37
44.54 2.02 1.74 43.34 8.36
21.62 1.44 1.09 59.90 15.95
U/Stress 3.68 0.12 0.27 9.95 85.97
Table 7.2: Prosodic marks showing prediction percentages for the composite prosody prediction
model.
(TO), and verbs be, were, was, been, are, is, have, had, has, can/may/would etc. (VB0{VM).
Refering to tables 7.1 and 4.3 it will be noted that for those word classes with poor performance
by the PPM that there is a widespread of use of the diering TSMs. For example DD1 scores 36%
in table 7.1. Table 4.3 shows that although it is mainly unstressed it also co{occurs with 4 TSMs
with roughly equal likelihood ( and ). RP also scores approximately 36% and table 4.3
shows that all 10 prosodic marks are plausible.
Whereas the models are very good at determining which words should be unstressed or which
words should have a stress accent the PPM is not able to choose the correct stress accent for all
nouns, adjectives, verbs (VV0-VVZ), adverbs, and determiners. This is not really surprising since
these word classes are those most related to the context and semantics of the utterance.
7.3.4 Prosodic Mark Models
The values presented in table 7.2 show the percentage of times that the predictions for each
prosodic mark match the corpus or not. For example is predicted as 38.88% of the time. The
ratios for each of the prosodic marks of the numbers of times that they exist in the corpus to the
number of times that they were predicted by the model are as follows:
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actual : predicted
2333 : 3245
653 : 173
1089 : 114
4237 : 4825
Unstress 7336 : 7291
Although there is a reasonable degree of accuracy for stress and unstress, rises and fall{rises
are very poorly modelled instead being replaced with falls or stressed marks. This is possibly
due to the high frequency of the bigram: fall followed by a tone unit boudary, which may bias
TSMs before a boundary away from being a rise or fall{rise. The fall mark has been largely over
predicted and to a lesser extent the stressed mark also. The model performs best for unstress,
stress and fall marks. In chapter 2 it was noted that the numberoflevels of stress that it is
usually useful to distinguish between is three (unstressed, weakly stressed and strongly stressed).
The model seems to add weight to this claim. Since fall, rise and fall{rise are all \strong" stresses
it is perhaps not surprising that the model does not perform well in distinguishing between them
and that one dominates over the others. Though in the case of fall{rise there is an approximately
equal split between the fall and stress mark which take the place of the fall{rise mark. For the
rise mark there is a 2:1 split between fall and stress marks. We can note that falls and rises are
\stronger" stress marks than fall{rises.
The results of the PPM suggest that the placement of stress accents is predictable from structure
and word class information but that the direction of the stress accent is not.
7.4 Future Work
During this research Ihave had many ideas which it has not been possibe to investigate either
due to lack of time or mainly because they do not belong within this research but are interesting
oshoots. These ideas are presented below in no particular order.
84

7.4.1 Conversion to ToBI
TOBI[SBP + 92, BA93] (Tones and Break Indices) is a modern system for transcribing prosodic
and intonation patterns in English and is attracting a lot of interest. Unlike previous prosodic
systems ToBI is being developed by alargenumberofspeech scientists with explicit application
to the annotation of machine readable spoken English corpora.
Roach[Roa94] has already provided a means of converting between the annotation scheme used
in the SEC and ToBI.
ToBI has two clear advantages. Firstly it has a grammar in the sense that some \sentences" of
annotations are not allowed and secondly it does not have unclear areas such as the stressed but
unaccented mark used in the SEC. A useful development tothework presented here would be to
convert the annotations produced to the ToBI system. This would allow for a phase of weeding
out illegal ToBI sequences from all those possibilities presented by the model and would decrease
the likelihood of the model producing unacceptible annotations. Given the amount ofinterest in
ToBI this would be a useful task to undertake especially if the model were ever to be used in a
text{to{speech system.
7.4.2 Additional Constraints
The focus of this thesis has been upon the relationships between prosodic annotations and word
class. It has often been noted that prosody serves more purposes than signifying stucture. We
can therefore expect marked improvements (especially in the prediction of stress accents) by the
inclusion of other factors than word class. Researchers in the eld of Natural Language Processing
have been developing semantic taggers for corpora | or systems that can annotate corpora with
semantic information derived from them[JA94]. As has been previously pointed out in this thesis
semantics play an important role in the structure of prosody. The inclusion of semantic information
in the prediction would be most advantageous in that, for example, given information could be
signied prosodically. At a lesser level parse tree structures could provide additional constraints.
85

It has also being noted by Knowles 2 that given information plays an important role in stress
assignment.
A further level of constraints could be imposed by rule in certain circumstances for example
stress accent assignment in compound nouns or noun phrases. Such constraints are discussed in
chapter 5 of Fudge[Fud84].
7.4.3 Speech Synthesis
There is at present no method for automatically synthesizing speech with appropriate pitch, intensity
and syllable durations for a given prosodic annotation. Usage of such a system, had it
existed, would have been most useful in assessing the acceptibility of annotations produced by the
model (see section 6.4).
There are very few text{to{speech systems that generate speech from phonemes and allow the
inclusion of prosodic annotations or pitch movement indications. One such system available (the
speech synthesizer 3 built into the Commodore Amiga personal computer) allows a specication of
stress level following each vowel ranging from 1 to 9. An attempt was made to convert between
the annotations produced by the model and stress level accepted by the synthesizer but there was
too little range of control as a general rise|fall contour was imposed by the system outside the
user's control. The stress level number merely perturbs this level upwards by avarying amount.
There is therefore no easy way of indicating a falling tone.
SOLA/PSOLA
An alternative tospeechsynthesis would be to use real speech and adjust these with the SOLA
and PSOLA algorithms to change the length and pitch contour. These are digital signal processing
techniques that allow a segment of speech tobechanged in duration without aecting the pitch
of the utterance (SOLA) and will allow the pitch of the speech tobechanged without aecting
the duration of the speech (PSOLA). These can be used to give any utterance any desired pitch
2 At a seminar given at Leeds University Linguistics Department. 1994
3 believed to be modeled on the DecTalk system.
86

contour and syllable duration. Intensity is modiable by scaling the waveform with an intensity
contour.
It is, however, very dicult for the novice to know how exactly to realise the prosodic annotations
in terms of F0 and intensity contours and syllable durations. There is also the additional
problem of correctly interpreting between F0 and intensity and pitch.
There is a very clear need for work on relating F0, intensity and duration to prosodic annotations.
7.4.4 Parameter Improvement
The model is dened in terms of the state probabilities and the transition probabilities. A large
portion of this research has concentrated upon methods to estimate these parameters directly from
the corpus. Improvements in the estimation of these parameters will improve the accuracy of the
model hence any means to perform this would be desireable.
There are other iterative methods that may be exploited to improve the parameters.
One
method would be to impose random variations in the parameters and observe how these eect the
overall performance of the model. Iteratively this would allow improvements to be made to the
poorly estimated parameters by keeping those changes that improved performance. Many other
methods exist. See for example Statistical Language Learning[Cha93].
7.5 General Conclusions
This whole thesis starts from the assumption that the prosodic annotations in the Spoken English
Corpus do have some correlation to the acoustic signal i.e. some \realisation" beyond the
perception of the human annotators. It is beyond the scope of this thesis to dene this prosodic{
acoustic correlation exactly but if and when others do this, then this thesis has mapped out the
link between prosodic and syntactic tags, and so will constitute a further link in the chain relating
acoustic signals to syntactic analysis.
87

Appendix A
SEC and MARSEC
A.1 Introduction
This appendix provides an outline description of the data and its organisation in the Spoken
English Corpus (SEC) and the Machine Readable Spoken English Corpus (MARSEC).
A.2 The Spoken English Corpus
A.2.1
History
The SEC was the product of a three{year project funded by IBM UK and carried out at the
University of Lancaster by Knowles et al[KT88] with the aim of providing a corpus of data for the
analysis of intonation.
The corpus comprises 52,673 words of text recorded principally from BBC Radio 4 broadcasts
and covers a diversity of categories in line with the LOB and BROWN corpora conventions.
A.2.2
Categories
The SEC is divided into 11 categories each category featuring a dierent variety ofspeechstyle.
These categories are listed in table A.1 with their size in words and as a percentage of the whole
88

corpus.
Each category is divided into a number of sections. Each section comprises of a single recording.
Table A.2 shows the section numbers (which begin with their category letter), their duration in
minutes and seconds and the approximate numberofwords in each section (accurate word counts
can sometimes be debateable).
A.3 MARSEC
The MARSEC was produced from a two{year ESRC project held jointly by Lancaster University
and Leeds University Roach[RKVA94]. The main dierence between the SEC and the MARSEC
is that the acoustic data has been added to the corpus in the form of a CD{ROM and fundamental
frequency and RMS energy have been calculated. The MARSEC also has the advantage of bringing
together all the available corpus material along with a segmental time alignment of the data and
a mechanism for cross{referencing (see Section 3.5).
The MARSEC update of the SEC therefore brings together the following information.
Digitized Recordings of the Speech
Fundamental Frequency
RMS Energy
Segmental Time Alignment with Syllabic Divisions
Prosodic Annotation with Time Alignment
Orthographic Text
Part of Speech Annotation
Parse Trees
Figure A.1 shows an example of all the available information for an example utterance. For
more detailed information see the references given above.
89

Category Style #words % corpus
A Commentary 9066 17%
B News Broadcast 5235 10%
C Lecture type I (general audience) 4471 8%
D Lecture type II (restricted audience) 7451 14%
E Religious (including liturgy) 1503 3%
F Magazine Style Reports 4710 9%
G Fiction 7299 14%
H Poetry 1292 2%
J Dialogue 6826 13%
K Propaganda 1432 3%
M Miscellaneous 3352 6%
Table A.1: Categories in the SEC/MARSEC
Section Time #Words
A01 15:00 793
A02 4:28 734
A03 4:01 620
A04 5:41 977
A05 4:48 804
A06 4:32 828
A07 3:54 716
A08 4:08 618
A09 5:12 787
A10 4:26 800
A11 4:15 785
A12 4:05 604
B01 9:32 1722
B02 9:40 1720
B03 5:00 940
B04 5:00 853
C01 30:00 4471
D01 19:00 2410
D02 19:00 2434
D03 19:00 2607
E01 6:48 915
E02 4:30 588
F01 3:48 671
F02 3:32 667
F03 4:54 850
F04 13:16 2522
Section Time #Words
G01 20:00 3163
G02 8:56 1221
G03 2:39 442
G04 5:30 810
G05 9:20 1663
H01 1:41 248
H02 2:03 286
H03 1:00 157
H04 2:59 405
H05 1:17 196
J01 7:58 1674
J02 1:31 279
J03 2:04 375
J04 0:27 74
J05 1:28 277
J06 24:00 4147
K01 4:32 798
K02 4:09 634
M01 0:41 93
M02 1:10 200
M03 0:48 140
M04 1:40 298
M05 4:33 738
M06 7:05 1112
M07 1:06 187
M08 0:47 143
M09 2:24 441
Table A.2: Sections in the SEC/MARSEC
90

[N a AT1 tiny JJ minority NN1 [P in II [N Argentina NP1 N]P]N]
Figure A.1: Diagram showing waveform, fundamental frequency, RMS energy, segmental, prosodic
and treebank transcriptions.
91

Appendix B
Syntactic Tagging of SEC
B.1 Introduction
This appendix provides a list of the part of speech word class tags used in the version of CLAWS
(CLAWS4) with which the SEC treebank version was annotated.
B.2 Word Class Tags
Wordtag Denition
! punctuation tag { exclamation mark
" punctuation tag { quotation marks
$ genitive sux(\'"or\'s")
&FO
formula
&FW
foreign word
( punctuation tag { left bracket
) punctuation tag { right bracket
, punctuation tag { comma
{ punctuation tag { dash
. punctuation tag { full stop
... punctuation tag { ellipsis
: punctuation tag { colon
punctuation tag { semicolon
punctuation tag { question mark
APP$
possessive pre-nominal pronoun: my, your, our
AT
neutral article: the, no
AT1
singular article: a, every
BTO
before-innitive marker: in order, so as before to
92

BTO21
idiom tag
BTO22
idiom tag
CC
general co-ordinating conjunction
CC31
idiom tag
CC32
idiom tag
CC33
idiom tag
CCB
co-ordinating conjunction but
CF
semi-co-ordinating conjunction: so, then, yet
CS
general subordinating conjunction
CS21
idiom tag
CS22
idiom tag
CSA
as as conjunction
CSN
than as conjunction
CST
that as conjunction
CSW
whether as conjunction
DA
neutral after-determiner capable of pronominal function: such
DA1
singular after-determiner: little, much
DA2
plural after-determiner: few, several, many
DA2R
comparative plural after-determiner: fewer
DAR
comparative neutral after-determiner: more, less
DAT
superlative neutral after-determiner: most, least
DB
before-determiner (capable of pronominal fn.): half, all
DB2
plural before-determiner: both without and
DD
neutral determiner capable of pronominal function: any, some
DD1
singular determiner: this, that, another
DD121
idiom tag
DD122
idiom tag
DD2
plural determiner: these, those
DD21
idiom tag
DD22
idiom tag
DD221
idiom tag
DD222
idiom tag
DDQ
`wh-' determiner without `-ever' : what, which
DDQ$
possessive `wh-' determiner: whose
DDQV
`wh-ever' determiner: whatsoever, whichever
EX
existential there
ICS
preposition-conjunction of time: after, before, since
IF
for as preposition
II
general preposition
II21
idiom tag
II22
idiom tag
II31
idiom tag
II32
idiom tag
II33
idiom tag
IO
of as preposition
IW
with, without as preposition
JA
predicative adjective: tantamount, afraid, asleep
JB
attributive adjective: late, model in a model prisoner
JBR
attributive comparative adjective: upper, outer
JJ
general adjective
JJR
general comparative adjective: older, better, stronger
JJT
general superlative adjective: oldest, best
LE
leading co-ordinator: either before or
MC cardinal: two, 6, 2.34
93

MC-MC hyphenated number: 1770-1827
MC1 singular cardinal number one, 1
MC2
plural cardinal number: threes, 3s
MD
ordinal number: second, 2nd, last
MF
fraction neutral for numbers: two-thirds
ND1
singular noun of direction: west
NN
common noun neutral for number: sheep, cod
NN1
singular common noun: book, girl
NN121
idiom tag
NN122
idiom tag
NN2
plural common noun: books, girls
NNJ
organization noun neutral for number: Company, group
NNJ1
singular organization noun: conference, Church
NNJ2
plural organization noun: groups, councils
NNL1
singular locative noun: island, Street
NNL2
plural locative noun: islands, streets
NNO
numeral noun, neutral for number agreement: dozen, hundred
NNO2
plural numeral noun: hundreds, millions
NNS1
singular titular noun: Mrs, President
NNS2
plural titular noun: Presidents
NNSA1
following abbrev. singular titular noun: M.A.
NNSB1
preceding abbrev. singular titular noun: Prof.
NNT1
singular temporal noun: day, week, year
NNT2
plural temporal noun: days, weeks, years
NNU
abbreviated unit of measurement neutral for number: in., kg
NNU1
singular unit of measurement: inch, kilo
NNU2
plural unit of measurement: ins., feet
NNU21
idiom tag
NNU22
idiom tag
NP
proper noun neutral for number: Andes, Indies
NP1
singular proper noun: London, Frederick
NP2
plural proper noun: Americas
NPD1
singular weekday noun: Thursday
NPM1
singular month noun: October
PN
indenite pronoun neutral for number: none
PN1
singular indenite pronoun: anybody, everyone, one as pronoun
PN121
idiom tag
PN122
idiom tag
PNQO
objective `wh-' pronoun without `-ever': whom
PNQS
`wh-' pronoun without `-ever' : who, that
PP$
nominal possessive personal pronoun: mine, yours
PPH1
it
PPHO1
him , her
PPHO2
them
PPHS1
he,she
PPHS2
they
PPIO1
me
PPIO2
us
PPIS1
I
PPIS2
we
PPX1
singular reexive personal pronoun: yourself, itself
PPX121
idiom tag
PPX122
idiom tag
PPX2
plural reexive personal pronoun: ourselves, themselves
94

PPX221
PPX222
PPY
RA
REX
REX21
REX22
RG
RG21
RG22
RGA
RGQ
RGQV
RGR
RGT
RL
RL21
RL22
RP
RR
RR21
RR22
RR31
RR32
RR33
RRQ
RRQV
RRR
RRT
RT
TO
UH
VBO
VBDR
VBDZ
VBG
VBM
VBN
VBR
VBZ
VDO
VDD
VDG
VDN
VDZ
VHO
VHD
VHG
VHN
VHZ
VM
VM21
VM22
VMK
idiom tag
idiom tag
you
adverb after nominal head: else, galore
adverb apposition-introducer: namely, e.g.
idiom tag
idiom tag
degree adverb: very, so, too
idiom tag
idiom tag
post-adjectival / adverbial degree adverb: enough, indeed
`wh-' degree adverb without `-ever' : how
`wh-ever' degree adverb: however
comparative degree adverb: more, less
superlative degree adverb: most, least
locative adverb: here, there
idiom tag
idiom tag
prepositional adverb which is also particle
general adverb
idiom tag
idiom tag
idiom tag
idiom tag
idiom tag
non-degree `wh-' adverb without `-ever': where, when, why
non-degree `wh-ever' adverb: wherever, whenever, however
comparative general adverb: better, longer
superlative general adverb: best, longest
nominal adverb of time: now, then
innitive marker to
interjection: hello, no
base form be
imperfect indicative were
was
being
am, 'm
been
are, 're
is, 's
base form do
did
doing
done
does
base form have
had, 'd (preterite)
having
had (past participle)
has, 's
modal auxiliary: can, may, would
idiom tag
idiom tag
modal catenative: ought, used
95

Labels
F
Fa
Fc
Fn
Fr
G
J
N
Nr
P
S
Si
T
Tg
Ti
Tn
V
Denition
nite clause, divided into:
adverbial clause
comparative clause
noun clause
relative clause
genitive phrase
adjectival phrase
noun phrase
temporal adverbial noun phrase
prepositional phrase
independent sentence (sentential conjuct)
interpolated or appended sentence
non-nite clause, divided into:
clause with present-participle head
clause with innitive head
clause with past-participle head
verb phrase (sequence of auxiliary & main verbs,
excl. object, complement, etc.)
Table B.1: Phrase and Clause labels
VV0
VVD
VVG
VVN
VVZ
XX
ZZ1
lexical verb, base form: eat, request
lexical verb, preterite: ate, requested
\-ing" present participle of lexical verb: giving
past participle of lexical verb: given
3rd singular form of verb: eats, requests
not, n't
singular letter of the alphabet
B.3 Phrase/Clause Tags
Table B.1 presents the list of phrase/clause node labels used in the treebank version of the SEC.
Note that some node labels will sometimes occur with a `&' or `+' sux in order to show the
coordination of phrases or clauses.
96

Appendix C
Testing Data
This appendices presents the text of those sections of category M that were used as testing data.
The results of which are presented in table 6.3. Also presented is the annotations produced by
the model.
C.1 Corpus Texts: Category M
Below are some of the prosodically annotated texts from Category M. Category M is the miscellaneous
section and the texts are therefore of a variety ofstyles. Not all of the texts were used
for testing. M01 was ommitted from the test set because it is a short peotry reading by John
Betjeman and is therefore an unusual style and M06 was ommitted because of a techinical problem
associated with alignment. It was not deemed necessary to have the complete category.
C.1.1
Section M02
046 SPOKEN ENGLISH CORPUS TEXT M02
Motoring News
Speaker: male
Broadcast notes: Radio 4, 8.55a.m., January 18th, 1987
97

Transcriber: BJW
in spite of the low j of the slow thaw j con ditions are probably more " dangerous
on the roads this morning j because yesterday's slush and " snow j is this morning's
ice k in Kent j the A two six four is still closed at " Blackham j as are many
side roads k and about a dozen of the more isolated villages j re main cut o k
a part from the weather j gas and
water main re pairs j to gether with j scheduled
weekend work j are also going to a ect the roads to day k and in London j
ex pect long delays in " Chiswick k where the west bound elevated section of the
M 4 j is
closed for most of to day j and only a single eastbound lane is open k in
Lancashire j northbound trac on the M 6 j will be re stricted to the
centre lane
only j between junctions thirty- one j and thirty- two from " Preston k to the M
fty- ve intersection until
4p.m. k and you can expect long queues there k at
10 o'clock this morning j the Thames Valley po lice are di verting all northbound
trac o the M 1 k and are leaving only one southbound lane open j between
junctions
fourteen and fteen j while a rather complicated re covery ope ration is
being carried out k so ex pect very long delays there k that's between junctions
fourteen j and
fteen j on the M 1this morning k
C.1.2
Section M03
047 SPOKEN ENGLISH CORPUS TEXT M03
Weather Forecast
Speaker: male
Broadcast notes: Radio 4, January 18th, 1987
Transcriber: BJW
98

now the weather forecast j#until dawn to morrow k over England and
Wales j
many places will be cloudy but dry k but in parts of Cornwall and west Wales j
a little light rain is likely j#which will ex tend into parts of Cumbria over night
k eastern Scotland j will have a dry j cloudy day j followed by rain or sleet j
to night k over Northern Ireland j and western Scotland j rain will ex tend from
the southwest j reaching Northern Ireland j early this morning j and western
Scotland soon after k outbreaks of rain j#will per sist j#into the night k but
after midnight j they're ex pected to clear j from Northern Ireland k eastern
areas will a gain be cold j but in the
west it'll be come less cold than recently
k and the outlook for Monday and Tuesday j mainly j dry and cold in the
south east j but re maining areas will be milder j with j a little rain k"so things
are im proving slightly there k
C.1.3
Section M04
048 SPOKEN ENGLISH CORPUS TEXT M04
Programme News
Speaker: male
Broadcast notes: Radio 4, January 18th, 1987
Transcriber: GOK
now let's look at our programmes j coming up on * Radio 4 this morning k
well j in a moment j in *"two-and-a- half minutes or therea bouts j there's the
news j followed j by our browse j through the Sunday papers k then at 9.1 5
j Alistair Cooke j pre sents this week's * Letter from A merica k our morning
service j at 9. 30 j comes this week j from Eneld j in Middlesex k and it's followed
j at 10.1 5 j by * a nother chance j to catch up with the week's goings on at
Ambridge k Margaret Howard j will be here with her * Pick of the Week j at a
99

quarter past e leven j and this week j we'll be hearing a bout *
camel wrestling
in Turkey j nights spent inthe Great Pyramids at Cairo j Hamlet j in Elsinore
j and tales j from wildest Canada j and Ecuador k add some * do-it-your self
Gilbert and Sullivan j and a re minder of the comedy of Al Reid j and that's
our Pick ofthe Week j at e leven f teen k the castaway j in Desert Island Discs
j one hour later j is silly-ass actor Jeremy Lloyd j who's also known j as a
scriptwriter k he's one half of the writing team j that cre ated * Are You Being
Served j and * 'A llo 'A llo k he'll be chatting a bout * chatting a bout his ca reer j to
Michael Parkinson j and picking his eightfavourite records j at "a quarter past
twelve k and that takes us to # lunchtime j and * The World at One k
nally j I've
just time j for a word a bout our Sunday feature j whichisa poetry programme
j with a dierence j Gardens of Eden j by Micheline Wonder k Maureen Lipman
plays Eve j Adam's rst wife j a ccording to the New Testament j and *
Miriam
Margolis j plays Lillith j who is Adam's rst wife j a ccording to the alphabet j
of Ben Seurat k their meeting in volves j a kind of life swap j Lillith j journeys
to the Old Testament j and takes tea with the Lord j while Eve decides j she's
had e nough j of being everyone's mother k
C.1.4
Section M05
049 SPOKEN ENGLISH CORPUS TEXT M05
Nelson Mandela speech
Speaker: Colin Lyas
Recorded at MSU, University of Lancaster
Transcriber: BJW
your Royal Highness and Chancellor j"it is my privilege to pre sent toyou j on
be half of the Senate j the name of Dr Nelson Man dela k the im prisoned leader
100

of the African National Congress j as one eminently worthy ofthede gree of
Doctor of Laws k Dr Mandela's life j has been de voted to the eort to se cure
j for all the citizens of his native South Africa j re gardless of their colour j
certain simple yet basic rights k the most fundamental of which is the right of
each of those who must o bey the law j to an equal voice within the po litical
system j under which the law iscreated k those who live in countries j where
the basic rights j that Dr Man dela seeks for his people have been won j owe
at the very least j a duty of sympathy j for those who have no such rights k
and they owe too j a duty ofres pect to those who j like Dr Man dela j have
un ceasingly striven in the face of hardship and danger j to claim those rights
k but in a ddition to what is owed to Dr Man dela by the citizens of any free
nation j any uni versity ina free society j owes him a special tribute k for in his
speeches and writings j Dr Man dela has un swervingly a sserted the cen trality
of an open edu cation j to the cultural life of any nation k he has in sisted
j as the Charter of the African National Congress puts it j that the doors of
learning and culture j shall be open k he has emphasised j that in a healthy
so ciety j young scholars are to be thought ofasa credit to their nation j and
not merely as a threat to its rulers k he has in sisted upon the pro foundly
liberalising e ects j of the meeting of the world's peoples j in open institutions
of learning k and to use his own words j he has re soundingly a rmed j that
for centuries uni versities have served j as centres j for the di ssemination of
learning and knowledge j to
all students j irre spective of colour and creed k in
multi-racial so cieties he continues j they serve asthe centres for the de velopment
of the cultural and spiritual aspects j of the life of the people k"the Charter
of this uni versity commands j that no test re lated to sex j race j colour
j or creed j shall be im posed upon any person j in order to entitle him j to
be ad mitted j as a member j teacher j or student k"for the members of this
101

uni versity j this charter en shrines a vic torious principle k and the fruits of that
victory can i mmediately be seen j in the international co mmunity of scholars
j that has graduated here to day k their presence has en riched this uni versity
and this country k and many will return home j to en hance their own nations k
but those who live by the principles of such charters as our own j owe a special
duty of testimony j to those j for whom the ght toachieve a recog nition of those
principles j has not been won k whose a llegiance to the principle of an open
educational system j in an open so ciety j is a con fession and a proclamation to be
paid for j in the coin of im prisonment j sepa ration j and even death k and
Nelson
Man dela has of course been willing to pay that price k"your Royal Highness and
Chancellor k at all times j there have been women and men j whose lives and
words have taken on a special meaning j to i nnumerably many oftheir fellow
human beings k their lives em body j and their words ar ticulate j the le gitimate
aspi rations of the de prived j the suering j and the slighted k Nelson Man dela j
has be come one such k and I can think of no better way to commend him to you
j than to use his own closing words j spoken in court j at the end of his nal trial
j when he was indeed facing the possi bility ofa sentence of death k during
my life I have dedicated my self to the struggle of the African people k Ihave
fought against white domi nation j and I have
fought against black domination
k Ihave cherished the i deal of a demo cratic and free so ciety k in which all
persons live to gether j in harmony j and with equal oppor tunities k it is an ideal
which I hope to live forandachieve j but if needs be j it is an ideal for which
I am pre pared to die k a life that has indeed been lived in the spirit of these
i deals j cannot but co mmand our re spect k and I therefore pre sent toyou the
name of Nelson Man dela j a las in ab sentia j as one
eminently worthy j of the
a ward of the de gree j of
Doctor of Laws j ho noris causa k
102

C.1.5
Section M07
051 SPOKEN ENGLISH CORPUS TEXT M07
Travel Roundup
Speaker: male
Broadcast notes: Radio 4, 8.55a.m., January 25th, 1987
Transcriber: GOK
" ve to nine now j and er * here's this morning's travel roundup j weekend
engi neering work j will cause problems j for both
road j and rail travellers to day
I'm a fraid k de lays can be ex pected j on the M8 in Glasgow j where there are lane
closures in " both di rections j between Kingston Bridge j and Charing Cross
tunnels k at junction 10 j the on ramp is closed j from Barty Beeth road j
to gether with two westbound lanes j on the
motorway itself k at " Manchester j
work on the M 6 2 j has closed the nearside lane j and hard shoulder j of both
carriageways j at junction seven teen j to wards Prestwich k and there are also j
eastbound lane closures j on the M5 6 leading from Cheshire j be tween junction
3 and 4 j at Altrincham k near Worcester j both carriageways of the M 5 are still
closed j following overnight work j be tween junctions 5and 6 j and di versions
are a long the A3 8 j until 10 this morning k on the " railways j engi neering work j
is widespread j with buses operating in stead of trains j on some routes k eastern
region trains j will be de layed j on the London King's Cross to Peterborough
line j as will western region services j on the Paddington j to Exeter route k I
hope you get there in the end k
C.1.6
Section M08
052 SPOKEN ENGLISH CORPUS TEXT M08
Weather forecast
103

Speaker: male
Broadcast notes: Radio 4, January 25th, 1987
Transcriber: GOK
here's the weather forecast for the U nited Kingdom until j
dawn to# morrow
k in
southern counties j of England and Wales j it'll be dull j but * early patches
of mist and drizzle j will # clear during this morning k over " northern England
j and Northern Ireland j it'll stay mainly cloudy j but dry j during to day j
and to night k in southern " Scotland after early sunshine in places j it'll be
mostly cloudy j but dry j al though to night there" may be some light rain k
northern Scotland j will have o ccasional light rain j which will be
followed during
the day j by colder j but still * mainly cloudy weather j with a few sleet and
snow showers k temperatures to day j will be * much as yesterday j ex cept
in northern Scotland j where it'll turn noticeably colder k and the outlook for
Monday and Tuesday j it'll be rather cold j in most places j with south western
areas j staying dry j but elsewhere j some light rain or sleet j is likely k
C.1.7
Section M09
053 SPOKEN ENGLISH CORPUS TEXT M09
Programme News
Speaker: male, Margaret Howard
Broadcast notes: Radio 4, January 25th, 1987
Transcriber: BJW
we'll just check j some of today's programmes j on Radio 4 j" for you j for our
Morning Service j at half past nine j we'll join the congre gation in the parish church
of St Faith in Great Cosby k and then at a quarter past ten j some Ambridge
104

ell-ringing to en joy k#just one highlight inthe lives of the Archers this week k
after that Margaret Howard j with her se lection j of listening highlights j from
the
past week's broadcasting k
CHANGE OF SPEAKER: MARGARET HOWARD
"the chief constable of Greater Manchester j James Anderton has been much
in the news this week k many interpre tations have been put on his statements
about being an instrument of" God k on Pick ofthe Week j you can hear j what
he actually said k Nigel Hawthorne j temporarily de serts Yes Prime Minister
j for a new role j on One Man and His Dog k he plays the part of a
ve year old
border collie k we recall the day that King George the Sixth j kept the Empire
waiting k David
Frost j e licits a cure for snoring j and we take a ride out with
a Bicester j in pur suit of the fox k
CHANGE OF SPEAKER: MALE
Margaret Howard k that's all in Pick ofthe Week j at a quarter past e leven
k an hour later j Baroness Ryder of Warsaw j joins Michael Parkinson j for a
stint on the old desert island k the Baroness heads the Sue Ryder foun dation
j which looks after the sick and di sabled in many parts of the world k and in
past years has frequently driven lorries full of medical supplies and pro visions
j to dis tressed areas of central Europe k all she has to do to day j is to pick
her eight desert island discs so j join her j and en joy hermusical se lection j
at a quarter past j#twelve k"this evening at a quarter past six j the rst of
two Actu ality proles j of some men and women recently re cruited j"to do
j voluntary overseas service k over the next two weeks j we'll join them j as
105

they're pre pared for j not only the ex citement of living in strange and e xotic
climes j but
also j the down side of things k
SPEECH EXTRACT OMITTED
well they all sound cheerful e nough j but for most VSO re cruits j the training
period is a mixture of fears j fantasies j and expec tations k as you can nd out
through Actu ality tonight at a quarter past
six k and just a re minder j that it's
Burns night k#if you're not planning to go out for a Burns night supper j then
stay in and en joy The
Miller's Reel j which be gins at a quarter past seven j this
evening k The Miller's Reel j takes the form of a love story j woven j from the
letters j poems j and songs of Robert Burns j and features the singing of Jean
Redpath and Rod Patterson k that's The Miller's Reel j e specially for Burns
night j# here on Radio 4 k
C.2 Prediction Results
C.2.1
Extract from section M05
The text presented below is an extract from section M05. This extract was not produced automatically
the output of the model is not in this format but as a list of symbols one per word class
signifying the prosodic annotation plus symbols for tone unit boundaries | which are not infact
predicted by the model. The simple rule a major boundary is generated by major punctuation
symbols (such as full stop, colon, semi{colon, exclamation mark, and question mark) and a minor
boundary by commas.
In this prediction refers to any non{accented stress such as or as well as the class stressed
but unaccented which is not well dened. Rises, falls and fall{rises (denoted by , and ) are not
distinguished here between high and low.
106

your Royal Highness and Chancellor k it is my privilege to pre sent toyou j on
be half of the Senate j the name of Dr Nelson Man dela k the im prisoned leader of
the African National Congress j as one eminently worthy ofthedegree of Doctor
of Laws 1 k Dr Mandela's lifeyhas been de voted to the eort to se cureyfor all the
citizens of his native South Africayre gardless of their
colour j certain simple yet
basic
rights k the most fundamental of which isthe right of each of those who
must o bey the lawyto an equal voice within the po litical systemyunder which the
law iscreated k
1 The lack of a fall here is probably due to the lack of a predicted tone unit boundary at the end of the predicted
stretch. A limit is placed on how many words are worked on at once. In this case the algorithm just missed the
end of sentence full stop that would have given rise to a tone unit boundary. Other missing boundaries are denoted
by y.
107

Appendix D
Word{Class / TSM Co{occurence
gures
The gures presented in this appendix were calculated from the sub{corpus sample used throughout
this research.
D.1 Tonic Stress Mark Frequencies.
Absolute ASCII Symbol
Frequency Symbol
12801 @ unstress
3511 *
2564 `
2297 ~
1528
1511 `/
1200 n
1158 ,
342 n ,
261 /
8 /`
108

D.2 Word Class Frequencies.
Freq. Tag
3536 NN1
2067 AT
2066 II
1673 JJ
1516 NN2
1299 NP1
884 IO
800 VV0
773 CC
771 RR
734 AT1
725 VVN
470 VVD
440 MC
409 TO
375 VVG
332 APP$
300 VM
283 DD1
281 VBDZ
267 IF
266 CST
262 PPH1
258 VBZ
208 NNT1
205 RP
192 VVZ
178 IW
172 NN
167 VB0
167 CCB
164 JB
158 DDQ
157 CS
152 VH0
150 MD
141 NNL1
130 VBR
130 PPHS1
120 ICS
119 PPHS2
118 RG
117 NNJ
Freq. Tag
114 VBN
110 RL
108 RT
106 PPIS2
99 VBDR
98 MC1
97 DD
93 XX
93 NNS1
93 CSA
92 NNT2
91 VHZ
88 EX
88 DB
87 PNQS
84 VHD
79 NNSB1
75 NNO
75 DD2
73 MF
72 RRQ
71 PPY
71 II22
71 II21
60 RR22
60 RR21
59 PPIS1
51 JJT
49 NNU2
46 DA
42 RRR
42 PPHO2
42 DAR
42 DA2
38 RGR
38 PN1
37 JJR
34 CF
32 VDD
32 NPM1
31 REX22
31 REX21
29 PPX1
Freq. Tag
28 VBG
28 RA
28 NNU21
27 NNU22
27 NNJ2
26 ZZ1
26 UH
25 VD0
25 DA1
25 &FW
24 CSN
23 RGT
23 PPIO2
23 ND1
18 NNL2
18 DAT
18 CS22
18 CS21
17 PPHO1
17 NNU1
17 NNJ1
16 LE
15 DB2
14 CSW
13 VDZ
12 PPX2
12 NNS2
12 DD222
12 DD221
11 VHG
11 MC2
10 PPIO1
10 NNU
10 II33
10 II32
10 II31
9 NPD1
8 VHN
8 RR33
8 RR32
8 RR31
8 RGQ
8 NP
Freq. Tag
7 VM21
7 RGA
7 JA
6 VDG
6 PP$
5 VDN
4 PN
4 NNO2
4 MC{MC
4 DDQV
4 DD22
4 DD21
4 BTO22
4 BTO21
3 RL22
3 RL21
3 DDQ$
3 DD122
3 DD121
2 RG22
2 RG21
2 PN122
2 PN121
2 NP2
2 NN122
2 NN121
2 JBR
2 &FO
1 VBM
1 RRT
1 RRQV
1 RGQV
1 PPX122
1 PPX121
1 PNQO
1 NNSA1
1 NNS
1 CC33
1 CC32
1 CC31
D.3 Tag/Tone Co-occurences
The following is a table showing all the occuring word class tags along with their frequncies of
co{occurence with TSMs low rise, high rise, low fall, high fall, low level, high level, low fall{rise,
high fall{rise, stressed but unaccented, and unstressed.
109

Tag
U/str
&FO 0 0 0 0 0 0 1 0 1 0
&FW 1 0 6 2 1 5 1 1 3 5
APP$ 1 0 2 9 1 8 0 7 6 303
AT 0 0 0 18 1 19 0 3 42 2016
AT1 1 0 0 1 1 3 0 1 12 739
BTO21 0 0 0 0 1 0 0 0 1 2
BTO22 0 0 0 0 1 0 0 0 0 3
CC 5 1 1 12 1 18 0 6 57 683
CC31 0 0 0 0 0 0 0 0 0 1
CC32 0 0 0 1 0 0 0 0 0 0
CC33 0 0 0 0 0 0 0 0 0 1
CCB 2 0 0 0 0 3 0 1 23 140
CF 4 0 0 1 0 3 1 1 5 19
CS 3 1 0 12 3 17 0 0 30 92
CS21 0 0 0 4 0 4 0 1 4 5
CS22 0 0 0 1 1 0 0 1 1 14
CSA 1 0 0 4 1 8 0 0 7 72
CSN 0 0 0 0 0 0 0 0 0 24
CST 0 0 0 0 0 0 0 0 8 260
CSW 1 0 0 4 1 0 0 0 7 1
DA 1 0 1 2 6 8 0 4 7 18
DA1 1 0 0 2 2 6 0 2 7 6
DA2 2 1 2 4 2 8 0 7 6 10
DAR 1 1 3 6 5 6 0 3 6 11
DAT 2 0 0 2 0 3 3 6 1 1
DB 0 1 9 18 5 19 2 14 14 8
DB2 0 0 0 1 0 4 0 6 2 2
DD 0 0 1 13 9 20 0 17 14 23
DD1 5 1 4 38 6 44 1 31 42 113
DD121 0 0 0 0 0 0 0 0 0 3
DD122 0 0 0 1 1 0 0 1 0 0
DD2 5 0 1 3 7 14 0 4 12 29
DD21 0 0 0 0 0 0 0 0 0 4
DD22 0 0 0 1 0 0 0 0 1 2
DD221 0 0 0 0 0 0 0 0 0 12
DD222 0 0 0 3 0 2 0 1 3 3
DDQ 3 0 1 10 7 11 0 1 22 106
DDQ$ 0 0 0 0 0 0 0 0 0 3
110

Tag
U/str
DDQV 0 0 0 3 0 0 0 1 0 0
EX 0 0 0 0 1 2 0 0 6 79
ICS 1 0 1 9 7 9 0 5 24 65
IF 1 0 0 1 1 0 0 0 11 254
II 7 1 11 25 13 47 1 25 128 1847
II21 1 0 0 9 4 7 1 6 16 26
II22 0 0 0 0 1 0 0 0 0 70
II31 0 0 0 0 0 0 0 0 0 11
II32 0 0 1 2 2 1 0 1 3 1
II33 0 0 0 0 0 0 0 0 0 11
IO 0 0 0 1 0 0 0 0 7 896
IW 0 0 0 4 1 5 0 3 12 157
JA 1 0 1 3 0 1 0 0 1 0
JB 7 0 3 23 18 28 1 20 40 24
JBR 0 0 0 0 0 1 0 1 0 0
JJ 68 15 87 263 200 354 37 177 356 145
JJR 2 0 2 5 4 8 1 11 1 3
JJT 1 1 2 12 3 9 0 14 3 6
LE 0 0 0 3 1 0 0 1 7 4
MC 12 2 22 78 63 108 4 35 73 50
MC-MC 0 0 0 1 0 2 0 0 0 1
MC1 4 1 2 10 11 22 0 12 18 18
MC2 3 0 1 3 0 0 1 1 2 0
MD 3 0 4 18 22 27 3 26 20 29
MF 3 0 2 13 1 10 0 1 16 24
ND1 1 0 5 4 2 2 0 1 5 3
NN 14 3 13 23 14 23 3 6 45 28
NN1 379 80 373 610 285 348 106 359 810 275
NN121 0 0 1 0 1 0 0 0 0 0
NN122 0 0 1 0 0 0 0 0 1 0
NN2 160 26 172 236 134 130 45 124 364 141
NNJ 7 5 6 20 12 3 7 8 30 19
NNJ1 1 2 3 2 2 2 0 1 3 1
NNJ2 3 0 1 4 3 4 1 2 7 2
NNL1 15 4 20 23 13 11 2 8 26 19
NNL2 0 0 2 3 6 2 1 0 2 2
NNO 3 0 2 9 7 4 0 10 14 26
NNO2 0 0 0 3 0 0 0 0 1 0
NNS 1 0 0 0 0 0 0 0 0 0
NNS1 3 3 3 8 9 15 2 5 31 13
NNS2 2 0 1 2 0 0 1 3 2 1
NNSA1 0 1 0 0 0 0 0 0 0 0
NNSB1 2 0 0 1 1 3 0 0 12 60
111

Tag
U/str
NNT1 15 3 19 32 13 15 4 12 62 35
NNT2 12 1 13 21 4 4 1 7 15 14
NNU 1 0 0 1 1 0 0 1 3 3
NNU1 2 2 1 3 0 1 2 1 3 2
NNU2 2 1 13 9 4 2 1 3 10 4
NNU21 0 0 0 0 0 0 0 0 1 27
NNU22 0 0 4 6 1 0 1 2 8 5
NP 0 0 0 1 1 1 0 0 1 4
NP1 91 52 129 191 131 191 37 155 222 117
NP2 0 0 0 2 0 0 0 0 0 0
NPD1 0 0 2 3 3 1 0 0 0 0
NPM1 1 0 7 9 1 1 2 6 3 2
PN 0 0 0 3 0 1 0 0 0 0
PN1 4 0 0 7 4 6 1 7 4 5
PN121 0 0 0 0 1 0 1 0 0 0
PN122 0 0 0 0 0 0 0 0 0 2
PNQO 0 0 0 0 0 0 0 0 1 0
PNQS 0 1 0 1 1 2 0 0 4 78
PP$ 0 0 0 1 0 0 0 0 0 5
PPH1 0 0 1 1 0 0 0 0 16 245
PPHO1 1 0 1 0 0 1 0 0 2 12
PPHO2 0 0 0 0 0 0 0 1 1 40
PPHS1 0 0 1 5 1 3 0 1 5 116
PPHS2 1 0 0 5 2 2 1 6 7 97
PPIO1 0 0 0 1 0 1 0 0 1 7
PPIO2 0 0 0 0 0 0 0 0 0 23
PPIS1 0 0 0 1 1 2 0 3 6 46
PPIS2 0 0 0 0 0 4 0 1 5 96
PPX1 1 0 5 6 2 1 4 4 7 1
PPX121 0 0 0 0 0 0 0 0 2 0
PPX122 0 0 1 0 0 0 0 0 1 0
PPX2 1 0 1 0 2 0 1 4 1 2
PPY 0 0 0 0 1 0 0 1 1 68
RA 2 0 4 3 0 3 1 2 7 6
REX21 0 0 0 0 0 0 0 0 1 30
REX22 3 1 1 1 3 0 0 1 20 1
RG 0 1 0 14 6 22 0 5 19 52
RG21 0 0 0 0 0 0 0 0 0 2
RG22 0 0 0 0 0 0 0 0 0 2
RGA 0 0 1 2 1 0 0 0 2 1
RGQ 1 0 0 0 1 1 0 0 1 4
RGQV 0 0 0 0 0 1 0 0 0 0
RGR 0 0 0 1 5 6 0 3 3 20
RGT 1 0 0 1 0 2 0 1 5 13
112

Tag
U/str
RL 8 1 16 13 10 15 3 4 26 15
RL21 0 0 0 0 0 0 0 0 0 3
RL22 0 1 0 1 0 0 0 1 0 0
RP 14 4 24 33 11 18 4 9 24 66
RR 41 5 28 157 65 130 16 83 132 117
RR21 0 0 0 0 1 1 0 0 7 51
RR22 7 1 3 14 2 5 0 6 16 6
RR31 0 0 0 0 0 0 0 0 3 5
RR32 0 0 2 4 1 0 0 0 0 1
RR33 0 0 0 0 1 0 0 0 0 7
RRQ 0 0 0 11 3 6 0 2 9 41
RRQV 0 0 0 0 0 0 0 0 0 1
RRR 3 0 7 3 6 7 1 6 8 1
RRT 0 0 0 0 0 1 0 0 0 0
RT 12 2 8 10 7 15 1 11 23 22
TO 0 0 0 0 0 0 0 0 3 410
UH 3 2 1 2 1 5 0 0 4 8
VB0 1 0 1 2 0 1 0 0 9 153
VBDR 0 0 0 2 0 6 0 1 1 90
VBDZ 0 0 0 2 3 4 0 1 11 261
VBG 0 0 0 0 0 2 0 0 4 22
VBM 0 0 0 1 0 0 0 0 0 0
VBN 0 1 0 1 2 1 0 0 5 104
VBR 0 0 1 8 4 3 0 2 5 108
VBZ 2 1 3 8 1 6 0 8 10 229
VD0 0 0 1 5 1 2 0 8 1 7
VDD 1 0 2 4 1 8 0 0 3 14
VDG 0 0 0 0 1 1 1 0 2 1
VDN 0 0 1 0 2 0 0 0 0 2
VDZ 0 0 0 2 1 2 0 0 3 5
VH0 1 0 0 3 3 4 0 5 13 123
VHD 0 0 0 2 1 6 0 0 3 75
VHG 0 0 0 0 2 1 0 0 2 6
VHN 0 0 0 2 1 0 0 0 2 3
VHZ 1 0 0 3 1 2 0 1 5 79
VM 2 0 5 20 9 19 0 10 17 218
VM21 0 0 0 2 0 2 0 0 2 1
VV0 67 7 47 104 90 105 9 47 185 144
VVD 13 10 21 53 47 85 2 28 147 67
VVG 21 3 6 54 51 71 5 20 105 42
VVN 67 8 62 107 96 115 16 56 152 60
VVZ 11 2 9 20 21 33 1 4 54 40
XX 0 1 4 21 6 22 0 8 10 22
113

Appendix E
Punctuation and Boundaries
Table E.1 shows the frequency of co{occurrence of puntuation with tone unit boudaries. fCTUg is
a symbol the means more than one punctuation symbol matched the same boundary the second
or third punctuation symbols are marked with this symbol. Punctuation that does not match any
tone unit boundary is marked with the fPNg symbol and tone unit boundaries that do not match
punctuation are marked with the fTUg symbol.
Analysis of the unusual cases such as the four full stops not coinciding with tone unit boundaries
often shows that the cases are questionable. In general the only punctuation symbols that do not
match tone unit boundaries are quotes and commas.
114

TAG fPNg k j * fCTUg
{ 1 21 69 0 14
! 1 13 1 0 0
1 40 2 1 1
. 4 1045 57 0 16
1 68 38 0 0
: 5 74 45 3 2
, 255 169 1425 13 6
( 2 6 26 0 1
) 3 0 19 0 13
" 56 18 44 14 102
... 1 2 1 0 1
fTUg N/A 55 3503 74 N/A
Table E.1: Punctuation/Tone Unit Boundary Co-occurence Table.
115

Appendix F
Source Code
F.1 symbolify.c
This program changes the symbols used for the prosodic annotation to the more iconic ASCII scheme
devised during this research. The were found to be two formats of prosodic annotation one which used
(generally unused) characters above ASCII 128 and another that used the sequence #xxx where xxx is
the ASCII value of the character used in the rst format. The original annotation system used a specially
modied character set that represented each of the prosodic symbols under a PC{based system but since
character sets like this are not generally available to most UNIX users this program remaps the symbols
into standard ASCII characters.
/******************************************************************************\
symbolify
- changes numbers to symbols in a SEC prosody transcription.
AUTHOR:
symbolify.c (c) Copyright January 1992, Simon Arnfield. All Rights Reserved.
SYNOPSIS:
symbolify [-h] prosody-filename
DESCRIPTION:
Gets prosody infomation from a SEC prosody transcription and converts to
ASCII symbols. Handles two different formats of prosody transcription:
Select second format with the -h switch.
First format uses byte codes to represent the different tonic stress marks
second format uses the string code #xxx to represent TSM symbols as listed
below.
String Octal Symbol Symbol name
#161 241 `/ high fall-rise
#162 242 /` high rise-fall
#246 366 ,\ low rise-fall --changed from 240 because of ^ problem
#247 367 \, low fall-rise
#171 253 , low rise
#172 254 / high rise
116

#174 256 ` high fall
#173 255 \ low fall
#163 243 ~ high level
_ low level
#248 370 * level stress (also #249, 371)
#165 245 > high reset
#166 246 < low reset
#240 360 ^ hesitation TU boundary (see low rise-fall)
| minor tone unit boundary
|| major tone unit boundary
FILES:
REFERENCES:
BUGS:
PROGRAM MODIFICATION HISTORY
Date | By | Vers | Comments
----------+-----+------+--------------------------------------------------------
11/02/92 | ScA | 1.0 | Created original code from extract-prosody.c
28/04/92 | ScA | 1.1 | Made alterations to numbers/symbols with new info
| | | of errors made in transcription.
03/07/92 | ScA | 1.2 | Added handling of byte codes and switch to select which
\******************************************************************************/
/*----------------------------*\
| INCLUDES AND DEFINITIONS |
\*----------------------------*/
#include
#include
#include
#define HASH 1
#define CHARACTER 2
/*-------------------------*\
| FUNCTIONS DEFINITIONS |
\*-------------------------*/
void error_exit(msg)
char *msg
{
printf("%s\n", msg)
exit(1)
}
void main(argc, argv)
int argc
char *argv[]
{
FILE * tag1
int fnameno = 1, ftype = CHARACTER
117

char cc[3]
unsigned char
c
if (argc != 2 && argc != 3)
error_exit("Usage: symbolify [-h] prosody-transcription-file")
if (argc == 3) {
fnameno = 2
if (!strcmp(argv[1], "-h"))
ftype = HASH
else
error_exit("Invalid switch")
}
if (!(tag1 = fopen(argv[fnameno], "r")))
error_exit("Filename error!")
while (!feof(tag1)) {
c = getc(tag1)
switch (ftype) {
case HASH:
if (c != '#')
putchar(c)
if (c == '#') {
fscanf(tag1, "%3s", cc)
if (!strcmp(cc, "161"))
printf("`/")
if (!strcmp(cc, "162"))
printf("/`")
if (!strcmp(cc, "246"))
printf(",\\")
if (!strcmp(cc, "247"))
printf("\\,")
if (!strcmp(cc, "171"))
printf(",")
if (!strcmp(cc, "172"))
printf("/")
if (!strcmp(cc, "173"))
printf("\\")
if (!strcmp(cc, "174"))
printf("`")
if (!strcmp(cc, "163"))
printf("~")
if (!strcmp(cc, "165"))
printf(">")
if (!strcmp(cc, "166"))
printf("

}
switch ((int)c) {
case 161:
printf("`/")
break
case 162:
printf("/`")
break
case 247:
printf("\\,")
break
case 246:
printf(",\\")
break
case 171:
printf(",")
break
case 172:
printf("/")
break
case 173:
printf("\\")
break
case 174:
printf("`")
break
case 163:
printf("~")
break
case 165:
printf(">")
break
case 166:
printf("

F.2 ttalign.c
See chapter 3 for a description of this program.
/******************************************************************************\
ttalign
- matches two word column files and associated tag and tone files
AUTHOR:
ttalign.c (c) Copyright November 1991, Simon Arnfield. All Rights Reserved.
SYNOPSIS:
ttalign tag-col-file tag-word-col-file tone-col-file tone-word-col-file
DESCRIPTION:
There are four input files derrived from the tagged and prosodic versions
of the sec with the following csh script:
# PROCESS_TAG_PROS. This script processes the vertical tag and prosodic
# files given in the input and outputs four files pros.[12] vtag.[12]
# ready for matching. Format: process_tag_pros tag-file pros-file
# e.g. process_tag_pros M01 M01.b
# tag file handling...
awk '$4=="-----" {next} \
$5=="("&&$6=="@" {b=1} \
$5=="[" {b=1} \
{if(b==0) print $4,$5} \
$5==")"&&$6=="@" {b=0} \
$5=="]" {b=0}' b=0 \
/usr/export/home/sca/work/corpus/tag/$1 | tee process.vtag \
| cut -f1 -d" " >vtag.1
cat process.vtag| cut -f2 -d" " | tr A-Z a-z >vtag.2
rm process.vtag
# prosody file handling...
symbolify /usr/export/home/sca/work/corpus/pros/$2 \
| awk '{a=substr($0,1,1)} {if (a!="[") print $0}' \
| tr -s ' ' '\012' \
| awk 'length!=0 {print $0}' \
| tee pros.1 \
| tr -d '`,/\\~_*@' | tr A-Z a-z >pros.2
This program attempts to match the words in the two files
pros.2 and vtag.2 and when it does so, prints out
the associated entry in the files pros.1 and vtag.1
The result is a list of words, the word with its tone marked on it,
and the part-of-speech that the word has in the corpus. These values
may then be used to calculate probabilities of co-occurrence of
tags and tones.
Several problems arise in this however, for example differences in
case between words in the vertical tag files and prosody files.
Words such as "don't" "won't" "it's" called enclitics, are treated as
two words "do + n't" "will + n't" in the vtag files but as single words
in the prosody file, meaning that the vertical output format will have
120

to have a blank entry for one column. Similar problems occur with compound
nouns, where "mother-in-law" takes only one entry in the vtag file but
may be marked as three lines (if hyphens are ommitted) in the prosdy file.
In addition it is possible to have a tone unit boundary (| or || or ^)that
does not co-incide with a punctuation symbol and vice-versa. These have
to add new blank entries in the appropriate columns.
New tags used are enclosed in {}.
TAG-TAGs TONE-TAGs WORD-TAGs Usage.
{PN}
no tone unit matching PuNctuation.
{TU}
{TONE-UNIT} no punc matching Tone Unit
{CP} {COMPOUND} lines following ComPound-nouns
{EN}
lines following ENclitics
Because, in some circumstances a bracket or quote will be next to
some punctuation such as a comma or full stop there is a need for
a post-processing phase to re-organise which punc symbol gets matched
to a tone-unit if one occurs. In these cases the bracket or quote is
given the symbol {CTU}. eg:
JJ philo*sophical philosophical
NN position position
*' {PN} '
NN /naturalism naturalism
**' | '
, {PN} ,
CC and and
will become:
JJ philo*sophical philosophical
NN position position
*' {PN} '
NN /naturalism naturalism
**' {CTU} '
, | ,
CC and and
FILES:
~sca/src/sec/symbolify.c
~sca/src/sec/collate-tu.c
~sca/work/corpus/pros/*
~sca/work/corpus/tag/*
These are similar to the original files in the corpus in /bjw /gok /dup
for the porsody, and /vtag for the tag, except that prosody files are
re-organised into one directory, names are changed, errors are corrected.
REFERENCES:
A manual of information to accompany the SEC Corpus.
L.J.Taylor, Dr. G.Knowles, 1988, Lancaster University.
BUGS:
Does not detect eof properly resulting in erronous lines at end of
output files.
PROGRAM MODIFICATION HISTORY
Date | By | Vers | Comments
----------+-----+------+--------------------------------------------------------
04/11/91 | ScA | 1.0 | Created original code
26/02/92 | ScA | 1.1 | Made handling of enclitics, punctuation, tone units
| | | and compound nouns.
121

02/03/92 | ScA | 1.2 | Several improvements, added handling of ( ) *' **'
06/03/92 | ScA | 1.3 | fixed some bugs in input handling.
06/04/92 | ScA | 1.4 | changed {} to differnt symbols for diff situations.
29/04/92 | ScA | 1.5 | removed {MW}, {BR}, made processing simpler
| | | by adding post-procesing phase
04/05/92 | ScA | 1.6 | tried to fix eof bug and multiple mismatch lines.
12/07/92 | ScA | 2.0 | added interactive fixing of mismatches.
12/08/92 | ScA | 2.1 | finished interactive fixing routines.
\******************************************************************************/
/*----------------------------*\
| INCLUDES AND DEFINITIONS |
\*----------------------------*/
#include
#include
#include
/*-------------------*\
| GLOBAL VARIABLES |
\*-------------------*/
char ntag[20][40], ntone[20][40],
ntag_word[20][40], ntone_word[20][40]
int b1 = 0, b2 = 0, b3 = 0, b4 = 0
/*-------------------------*\
| FUNCTIONS DEFINITIONS |
\*-------------------------*/
void print(str1, str2, str3)
char *str1, *str2, *str3
{
printf("%-8s%-24s%-24s\n", str1, str2, str3)
fprintf(stderr, "%-8s%-24s%-24s\n", str1, str2, str3)
}
char *read_tag1(file)
/* provides tag input from the tag files. */
FILE *file
{
char input[40]
int i
if (b1 == 0)
if (feof(file))
strcpy(input, "")
else
fscanf(file, "%s", input)
else {
strcpy(input, ntag[1])
for (i = 2 i

char *read_tag2(file)
/* provides word input from the tag files. */
FILE *file
{
char input[40]
int i
if (b2 == 0)
if (feof(file))
strcpy(input, "")
else
fscanf(file, "%s", input)
else {
strcpy(input, ntag_word[1])
for (i = 2 i

}
for (i = 2 i b1)
fscanf(tag1, "%s", ntag[++b1])
if ((++bb2) > b2)
fscanf(tag2, "%s", ntag_word[++b2])
if ((++bb3) > b3)
fscanf(pros1, "%s", ntone[++b3])
if ((++bb4) > b4)
fscanf(pros2, "%s", ntone_word[++b4])
} while ((b1 < 10) && (b3 < 10) && strcmp(ntag_word[bb2],
tone_word) && strcmp(ntone_word[bb4], tag_word) && !feof(tag1) &&
!feof(pros1))
if ((punc || tu) || (strcmp(ntag_word[b2], tone_word) &&
strcmp(ntone_word[b4], tag_word))) {
124

* if tag is a punctuation symbol or if tone is a tone unit */
/* then it is possible that the previous mismatch is part of */
/* an enclitic or compound followed by punctuation or a tone */
/* unit the PUNC or TU may then be found to match a TU or PUNC */
/* later on this is where the automatching gets complicated */
/* so, ask the user for assistance interactively. Note that we */
/* ignore the reults of the bit above - as far as we are */
/* concerend here we only want the buffers full so we can give */
/* the user some context. */
/* interactive fixit */
int i, finished = 0
char *command[256]
do {
fprintf(stderr, "FIXITMODE: Tone next(1-TU,2-CP).
Tags next(3-EN,4-PN). 5:Exit\n")
fprintf(stderr, " 0# %-8s%-24s%-24s\n",
tag, tone, tag_word)
for (i=1,j=1(i

eak
case 3:
print(tag, "{EN}", tag_word)
strcpy(tag, ntag[1])
strcpy(tag_word, ntag_word[1])
for (i = 2 i

char tag[40], tone[40], tag_word[40], pros_word[40]
int punc = 0, tu = 0, new_data = 0
if (argc != 5) {
fprintf(stderr, "usage: ttalign tag tag-word tone tone-word\n
Output produced by PROCESS_TAG_PROS")
exit(1)
}
if (!(tag1 = fopen(argv[1], "r"))) {
fprintf(stderr, "Can't open %s\n", argv[1])
exit(1)
}
if (!(tag2 = fopen(argv[2], "r"))) {
fprintf(stderr, "Can't open %s\n", argv[2])
exit(1)
}
if (!(pros1 = fopen(argv[3], "r"))) {
fprintf(stderr, "Can't open %s\n", argv[3])
exit(1)
}
if (!(pros2 = fopen(argv[4], "r"))) {
fprintf(stderr, "Can't open %s\n", argv[4])
exit(1)
}
while (!feof(tag1) && !feof(pros1)) {
if (!new_data) {
strcpy(tag, read_tag1(tag1))
strcpy(tag_word, read_tag2(tag2))
strcpy(tone, read_pros1(pros1))
strcpy(pros_word, read_pros2(pros2))
}
new_data = 0
if (strstr(".,:!*-...*'**'()", tag))
punc = 1
else
punc = 0
if (strstr("||^|", tone))
tu = 1
else
tu = 0
if (!strcmp(pros_word, tag_word) || (punc && tu))
print(tag, tone, tag_word)
else {
if (!punc && !tu) /* neither punc or tu and don't match */
handle_unmatched(tag, tone, tag_word,
pros_word, tag1, tag2, pros1, pros2)
if (punc && !tu) /* punctuation but no tu boundary */ {
print(tag, "{PN}", tag_word)
if (!feof(tag1)) {
strcpy(tag, read_tag1(tag1))
strcpy(tag_word, read_tag2(tag2))
new_data = 1
}
}
if (!punc && tu) /* tu boundary but no punctuation */ {
print("{TU}", tone, "{TONE-UNIT}")
if (!feof(pros1)) {
127

}
}
}
}
strcpy(tone, read_pros1(pros1))
strcpy(pros_word, read_pros2(pros2))
new_data = 1
}
fclose(pros2)
fclose(pros1)
fclose(tag2)
fclose(tag1)
/*-------*\
| END |
\*-------*/
F.3 collate-tu.c
This program is used as a post{processing phase to ttalign and specifcally handles cases where there
are multiple punctuation symbols that co{incide with a tone unit boundary. As wellasalittleshuing
to ensure that the tone unit boundary is aligned with the primary type of punctuation (viz. brackets
and quotes are less likely to give rise to a boundary than, say, a full stop or comma) it marks the other
punctuation symbols as matching a boundary whereas ttalign would have left the punctuation marked as
punctuation that does not match a boundary | which would be incorrect.
/******************************************************************************\
collate-tu - collects together punctuation under one TU, where appropriate
AUTHOR:
collate-tu.c (c) Copyright May 1992, Simon Arnfield. All Rights Reserved.
SYNOPSIS:
collate-tu
DESCRIPTION:
Because, in some circumstances a bracket or quote will be next to
some punctuation such as a comma or full stop there is a need for
a post-processing phase to re-organise which punc symbol gets matched
to a tone-unit if one occurs. In these cases the bracket or quote is
given the symbol {CTU}. eg:
JJ philo*sophical philosophical
NN position position
*' {PN} '
NN /naturalism naturalism
**' | '
, {PN} ,
CC and and
will become:
JJ philo*sophical philosophical
NN position position
*' {PN} '
NN /naturalism naturalism
128

**' {CTU} '
, | ,
CC and and
PROGRAM MODIFICATION HISTORY
Date | By | Vers | Comments
----------+-----+------+--------------------------------------------------------
04/05/92 | ScA | 1.0 | Created original code
\******************************************************************************/
/*----------------------------*\
| INCLUDES AND DEFINITIONS |
\*----------------------------*/
#include
#include
#include
/*-------------------------*\
| FUNCTIONS DEFINITIONS |
\*-------------------------*/
void main(argc, argv)
int argc
char *argv[]
{
FILE * file
char pwrd1[8], pwrd2[24], pwrd3[24]
char wrd1[8], wrd2[24], wrd3[24]
int p_is_tu = 0, is_pn = 0, flag = 0
if (argc != 2) {
printf("usage: collate-tu \n")
exit(1)
}
if (!(file = fopen(argv[1], "r"))) {
printf("Can't open %s\n", argv[1])
exit(1)
}
fscanf(file, "%s", pwrd1)
fscanf(file, "%s", pwrd2)
fscanf(file, "%s", pwrd3)
while (!feof(file)) {
fscanf(file, "%s", wrd1)
fscanf(file, "%s", wrd2)
fscanf(file, "%s", wrd3)
if (strstr("|^||", pwrd2))
p_is_tu = 1
else
p_is_tu = 0
if (!strcmp("{PN}", wrd2))
is_pn = 1
else
is_pn = 0
129

flag = 0
if ((!strcmp("'", pwrd1) || !strcmp(")", pwrd1)) &&
p_is_tu && is_pn) {
printf("%-8s%-24s%-24s\n", pwrd1, "{CTU}",
pwrd3)
printf("%-8s%-24s%-24s\n", wrd1, pwrd2,
wrd3)
fscanf(file, "%s", pwrd1)
fscanf(file, "%s", pwrd2)
fscanf(file, "%s", pwrd3)
flag = 1
}
if (!flag && p_is_tu && is_pn) {
printf("%-8s%-24s%-24s\n", pwrd1, pwrd2,
pwrd3)
printf("%-8s%-24s%-24s\n", wrd1, "{CTU}",
wrd3)
fscanf(file, "%s", pwrd1)
fscanf(file, "%s", pwrd2)
fscanf(file, "%s", pwrd3)
while (!strcmp("{PN}", pwrd2)) {
printf("%-8s%-24s%-24s\n", pwrd1,
"{CTU}", pwrd3)
fscanf(file, "%s", pwrd1)
fscanf(file, "%s", pwrd2)
fscanf(file, "%s", pwrd3)
}
flag = 1
}
}
if (!flag) {
printf("%-8s%-24s%-24s\n", pwrd1, pwrd2,
pwrd3)
strcpy(pwrd1, wrd1)
strcpy(pwrd2, wrd2)
strcpy(pwrd3, wrd3)
}
}
fclose(file)
/*-------*\
| END |
\*-------*/
F.4 align-parse.c
align-parse takes the output from ttalign and the parsetree le from which tags were taken and inserts
the phrase brackets at the appropriate place in the alignle. That is it produces a le with the phrase
brackets and tags aligned with the prosodic words and tone unit boundaries. This somewhat trivial task is
confused with the need to check for tone unit boundaries or the absence of and insert lines appropriately.
/******************************************************************************\
130

align-parse - aligns sec parsetree with output from ttalign
AUTHOR:
align-parse (c) Copyright November 1991, Simon Arnfield. All Rights Reserved.
SYNOPSIS:
align-parse
parse-file tag-tone-file
DESCRIPTION:
Aligns a sec parsetree with the output produced by ttalign. That is it
produces a file containing phrase brackets and words aligned with tone-unit
boundaries and words in the output from the tag-tone alignment.
IDIOSYNCRASIES:
FILES:
REFERENCES:
A manual of information to accompany the SEC Corpus.
L.J.Taylor, Dr. G.Knowles, 1988, Lancaster University.
BUGS:
PROGRAM MODIFICATION HISTORY
Date | By | Vers | Comments
----------+-----+------+--------------------------------------------------------
07/03/92 | ScA | 1.0 | Created original code
06/08/92 | ScA | 1.1 | changed because ttalign is now used with treebank
17/08/92 | ScA | 1.2 | added recognition of compounds as these will skew
| output in same way as TU would, if lines are matched.
| | | data. Therefore word-tags are same.
\******************************************************************************/
/*----------------------------*\
| INCLUDES AND DEFINITIONS |
\*----------------------------*/
#include
#include
#include
/*-------------------------*\
| FUNCTIONS DEFINITIONS |
\*-------------------------*/
void main(argc, argv)
int argc
char *argv[]
{
FILE * tag1, *tag2
char input1[80], input1b[40], in2a[32], in2b[32], in2c[32]
if (argc != 3) {
printf("usage: align-parse parse-file tag-tone-file\n")
exit(1)
}
131

if (!(tag1 = fopen(argv[1], "r"))) {
printf("Can't open %s\n", argv[1])
exit(1)
}
if (!(tag2 = fopen(argv[2], "r"))) {
printf("Can't open %s\n", argv[2])
exit(1)
}
fscanf(tag1, "%s", input1)
fscanf(tag2, "%s", in2a)
fscanf(tag2, "%s", in2b)
fscanf(tag2, "%s", in2c)
while (!feof(tag1) && !feof(tag2)) {
/* if in2a = {TU} then must insert blank line in col 1 */
if (!strcmp(in2a, "{TU}")) {
printf("%-40s%-6s%-16s%-18s\n", "", in2a,
in2c, in2b)
fscanf(tag2, "%s", in2a)
fscanf(tag2, "%s", in2b)
fscanf(tag2, "%s", in2c)
}
/* if in2a = {CP} treat as {TU} */
/* BUT can have sequences of more than one {CP} */
while (!strcmp(in2a, "{CP}")) {
printf("%-40s%-6s%-16s%-18s\n", "", in2a,
in2c, in2b)
fscanf(tag2, "%s", in2a)
fscanf(tag2, "%s", in2b)
fscanf(tag2, "%s", in2c)
}
/* if input1 has [ or ] in it then add next line
/* otherwise align with input2*/
if (strstr(input1, "[") || strstr(input1, "]")) {
fscanf(tag1, "%s", input1b)
if (strstr(input1b, "[") || strstr(input1b,
"]")) {
strcat(input1, input1b)
fscanf(tag1, "%s", input1b)
} else
printf("%-40s%-6s%-16s%-18s\n",
input1, in2a, in2c, in2b)
} else
printf("%-40s%-6s%-16s%-18s\n", "", in2a,
in2c, in2b)
}
fscanf(tag1, "%s", input1)
fscanf(tag2, "%s", in2a)
fscanf(tag2, "%s", in2b)
fscanf(tag2, "%s", in2c)
}
fclose(tag2)
fclose(tag1)
132

*-------*\
| END |
\*-------*/
F.5 splittule.c
This program provided a front end to probabilityc by splitting the align les produced by ttalign into many
smaller les and generating a c{shell script to execute probabilityc on these les. This is used for testing
probabilityc and for producing results. It is not intended as a real front end to the model | only a front
end to allow testing. Splitting the input into lots of les is very dirty programming but allows recovery of
a run in case the machine goes down whilst the lengthy calculations take place. A nicer front end should
be written for use with the model for synthesis purposes.
/* splittufile.c Copyright Simon Arnfield August 1993 */
/* this program converts align files to many files suitable for use with */
/* probabilityc.c it also generates (on stdout) a script to process these */
/* the align files must first have been processed with the following script */
/* cat ~/work/corpus/align/a01.align|grep -v "{TU}"| grep -v "{CTU}"| \ */
/* grep -v '^$ {EN}' |grep -v "XXXX"|splittufile|csh >a01.results */
/* we break the input either at punctuation or when the file is MAXWRD long */
#include
#include
#define MAXWRD 12
main()
{
char
bufw[MAXWRD][99], buft[MAXWRD][99], filename[9], word[99],
tag[99], tmp[99]
int pos = 0, i, filenum = 1
FILE * file
sprintf(filename, "file%d", filenum)
while (!feof(stdin)) {
scanf("%s %s %*s", tag, word)
if (strstr("!(),-.:...'", tag) && strstr("!(),-.:...'",
buft[pos-1]) && pos >= 1) {
pos--
strcpy(tmp, buft[pos])
strcat(tmp, tag)
strcpy(tag, tmp)
strcpy(tmp, bufw[pos])
strcat(tmp, word)
strcpy(word, tmp)
}
strcpy(buft[pos], tag)
strcpy(bufw[pos], word)
pos++
if ((pos == MAXWRD || strstr("!(),-.:...'", tag)) &&
pos > 3) {
if ((file = fopen(filename, "w")) == 0) {
fprintf(stderr, "couldn't open file %s\n",
filename)
exit(1)
}
fprintf(file, "%d\n", pos)
133

}
}
}
for (i = 0 i < pos i++)
fprintf(file, "%s %s\n", buft[i],
bufw[i])
fclose(file)
printf("probabilityc

| INCLUDES AND DEFINITIONS |
\*----------------------------*/
#include
#include
#include
/*-------------------*\
| GLOBAL VARIABLES |
\*-------------------*/
char tags[187][7] = {
"&FO", "&FW", "APP$", "AT", "AT1", "BTO", "BTO21", "BTO22",
"CC", "CC31", "CC32", "CC33", "CCB", "CF", "CS", "CS21",
"CS22", "CSA", "CSN", "CST", "CSW", "DA", "DA1", "DA2",
"DA2R", "DAR", "DAT", "DB", "DB2", "DD", "DD1", "DD121",
"DD122", "DD2", "DD21", "DD22", "DD221", "DD222", "DDQ",
"DDQ$", "DDQV", "EX", "ICS", "IF", "II", "II21", "II22",
"II31", "II32", "II33", "IO", "IW", "JA", "JB", "JBR",
"JJ", "JJR", "JJT", "LE", "MC", "MC-MC", "MC1", "MC2",
"MD", "MF", "ND1", "NN", "NN1", "NN121", "NN122", "NN2",
"NNJ", "NNJ1", "NNJ2", "NNL1", "NNL2", "NNO", "NNO2",
"NNS", "NNS1", "NNS2", "NNSA1", "NNSB1", "NNT1", "NNT2",
"NNU", "NNU1", "NNU2", "NNU21", "NNU22", "NP", "NP1",
"NP2", "NPD1", "NPM1", "PN", "PN1", "PN121", "PN122",
"PNQO", "PNQS", "PP$", "PPH1", "PPHO1", "PPHO2", "PPHS1",
"PPHS2", "PPIO1", "PPIO2", "PPIS1", "PPIS2", "PPX1",
"PPX121", "PPX122", "PPX2", "PPX221", "PPX222", "PPY",
"RA", "REX", "REX21", "REX22", "RG", "RG21", "RG22",
"RGA", "RGQ", "RGQV", "RGR", "RGT", "RL", "RL21", "RL22",
"RP", "RR", "RR21", "RR22", "RR31", "RR32", "RR33", "RRQ",
"RRQV", "RRR", "RRT", "RT", "TO", "UH", "VB0", "VBDR",
"VBDZ", "VBG", "VBM", "VBN", "VBR", "VBZ", "VD0", "VDD",
"VDG", "VDN", "VDZ", "VH0", "VHD", "VHG", "VHN", "VHZ",
"VM", "VM21", "VM22", "VMK", "VV0", "VVD", "VVG", "VVN",
"VVZ", "XX", "ZZ1", "{CP}", ".", ":", "", "!", "",
",", "-", "(", ")", "'"}
int trUU[190][190], trUS[190][190], trSU[190][190], trSS[190][190]
/*-------------------------*\
| FUNCTIONS DEFINITIONS |
\*-------------------------*/
void main()
{
char tag[7], prosody[40], word[40]
int i, j, t1 = -1, t2 = -1, s1, s2
for (i = 0 i < 170 i++) {
for (j = 0 j < 170 j++) {
trUU[i][j] = 0
trUS[i][j] = 0
trSU[i][j] = 0
trSS[i][j] = 0
}
}
135

* assume first tag is ok - we don't check if it IS 0-187 */
scanf("%s %s %s", tag, prosody, word)
for (i = 0 i < 187 i++)
if (!strcmp(tag, tags[i]))
t1 = i
for (s1 = 0, j = 0 j < strlen(prosody) j++)
if (prosody[j] == '*' || prosody[j] == ',' || prosody[j]
== '/' || prosody[j] == '\\' || prosody[j] =='`'||
prosody[j] == '~' || prosody[j] == '_') {
s1 = 1
break
}
while (!feof(stdin)) {
scanf("%s %s %s", tag, prosody, word)
for (t2 = -1, i = 0 i < 187 i++)
if (!strcmp(tags[i], tag))
t2 = i
if (t2 == -1) {
printf("Error: %s\n", tag)
scanf("%s %s %s", tag, prosody, word)
for (t2 = -1, i = 0 i < 187 i++)
if (!strcmp(tags[i], tag))
t2 = i
}
for (s2 = 0, j = 0 j < strlen(prosody) j++)
if (prosody[j] == '*' || prosody[j] == ',' ||
prosody[j] == '/' || prosody[j] == '\\' ||
prosody[j] == '`' || prosody[j] == '~' ||
prosody[j] == '_')
s2 = 1
}
if (s1 == 0 && s2 == 0)
trUU[t1][t2]++
if (s1 == 0 && s2 == 1)
trUS[t1][t2]++
if (s1 == 1 && s2 == 0)
trSU[t1][t2]++
if (s1 == 1 && s2 == 1)
trSS[t1][t2]++
t1 = t2
s1 = s2
for (i = 0 i < 187 i++) {
for (j = 0 j < 187 j++)
printf("%d ", trUU[i][j])
printf("\n")
}
for (i = 0 i < 187 i++) {
for (j = 0 j < 187 j++)
printf("%d ", trUS[i][j])
136

}
printf("\n")
for (i = 0 i < 187 i++) {
for (j = 0 j < 187 j++)
printf("%d ", trSU[i][j])
printf("\n")
}
}
for (i = 0 i < 187 i++) {
for (j = 0 j < 187 j++)
printf("%d ", trSS[i][j])
printf("\n")
}
/*-------*\
| END |
\*-------*/
F.7 transgroups.c
Transgroups has hard{wired into it the group denitions (i.e. which tags belong to which groups). It takes
as input the transitions.table produced by the transitions program. It uses the group denitions and the
transitions.table to produce the group{to{group transition probabilities. That is the probability that a tag
in group G1 will have stress state S1 and be followed by a tag in group G2 which has a stress state S2. S1
and S2, in this case, are either stressed or unstressed. The resulting probabilities are used in proabilityc.
/******************************************************************************\
transgroups - adds up values in transitions.table for given group transitions
AUTHOR:
transgroups.c (c) Copyright May 1993, Simon Arnfield. All Rights Reserved.
SYNOPSIS:
transgroups

*----------------------------*\
| INCLUDES AND DEFINITIONS |
\*----------------------------*/
#include
#include
#include
#define NUMGROUPS 10
/*-------------------------*\
| FUNCTIONS DEFINITIONS |
\*-------------------------*/
int whichgroup(tg) /* returns group number for tag number tg */
int tg
{
switch (tg) {
case 177: case 178: case 179: case 180: case 181:
case 182: case 183: case 184: case 185: case 186:
return(0)
case 1: case 2: case 8: case 9: case 10:
case 11: case 44: case 51: case 95: case 96:
case 97: case 98: case 101: case 103: case 105:
case 146: case 148: case 150: case 151: case 154:
case 155: case 156: case 157: case 158: case 159:
case 161: case 162: case 163: case 164:
return(1)
case 41: case 43: case 99: case 100: case 102:
case 107: case 108: case 109: case 110: case 147:
case 149: case 152:
return(2)
case 3: case 4: case 5: case 6: case 7:
case 18: case 19: case 46: case 47: case 49:
case 50: case 117: case 145:
return(3)
case 12: case 13: case 14: case 15: case 16:
case 17: case 20: case 38: case 39: case 40:
case 42: case 58: case 81: case 82: case 104:
case 106: case 153: case 160: case 165: case 166:
case 167: case 168:
return(4)
case 21: case 22: case 23: case 24: case 25:
case 26: case 27: case 28: case 52: case 53:
case 54: case 55: case 56: case 57: case 66:
case 90: case 91: case 92: case 118: case 130:
case 131: case 132: case 134: case 135: case 136:
case 137: case 138: case 139: case 140: case 141:
case 142: case 143: case 144: case 169: case 172:
case 175:
return(5)
138

case 0: case 29: case 59: case 60: case 61:
case 62: case 63: case 174:
return(6)
case 78: case 79: case 80: case 170: case 171:
case 173:
return(7)
case 65: case 67: case 68: case 69: case 70:
case 71: case 72: case 73: case 74: case 75:
case 83: case 84: case 111: case 112: case 113:
case 114: case 115: case 116: case 176:
return(8)
case 30: case 31: case 32: case 33: case 34:
case 35: case 36: case 37: case 45: case 48:
case 64: case 76: case 77: case 85: case 86:
case 87: case 88: case 89: case 93: case 94:
case 119: case 120: case 121: case 122: case 123:
case 124: case 125: case 126: case 127: case 128:
case 129: case 133:
return(9)
}
}
void main()
{
int i, j, val, g1, g2,
gpUU[NUMGROUPS][NUMGROUPS], gpUS[NUMGROUPS][NUMGROUPS],
gpSU[NUMGROUPS][NUMGROUPS], gpSS[NUMGROUPS][NUMGROUPS]
float T = 0
for (i = 0 i < NUMGROUPS i++) {
for (j = 0 j < NUMGROUPS j++) {
gpUU[i][j] = 0
gpUS[i][j] = 0
gpSU[i][j] = 0
gpSS[i][j] = 0
}
}
for (i = 0 i < 187 i++)
for (j = 0 j < 187 j++) {
scanf("%d", &val)
g1 = whichgroup(i)
g2 = whichgroup(j)
gpUU[g1][g2] +=val
}
for (i = 0 i < 187 i++)
for (j = 0 j < 187 j++) {
scanf("%d", &val)
g1 = whichgroup(i)
g2 = whichgroup(j)
gpUS[g1][g2] +=val
139

}
for (i = 0 i < 187 i++)
for (j = 0 j < 187 j++) {
scanf("%d", &val)
g1 = whichgroup(i)
g2 = whichgroup(j)
gpSU[g1][g2] +=val
}
for (i = 0 i < 187 i++)
for (j = 0 j < 187 j++) {
scanf("%d", &val)
g1 = whichgroup(i)
g2 = whichgroup(j)
gpSS[g1][g2] +=val
}
for (i = 0 i < NUMGROUPS i++) {
for (j = 0 j < NUMGROUPS j++) {
T = gpUU[i][j] + gpUS[i][j] + gpSU[i][j]
+ gpSS[i][j]
printf("%5.4f,", (float)gpUU[i][j] / T)
}
printf("\n")
}
for (i = 0 i < NUMGROUPS i++) {
for (j = 0 j < NUMGROUPS j++) {
T = gpUU[i][j] + gpUS[i][j] + gpSU[i][j]
+ gpSS[i][j]
printf("%5.4f,", (float)gpUS[i][j] / T)
}
printf("\n")
}
for (i = 0 i < NUMGROUPS i++) {
for (j = 0 j < NUMGROUPS j++) {
T = gpUU[i][j] + gpUS[i][j] + gpSU[i][j]
+ gpSS[i][j]
printf("%5.4f,", (float)gpSU[i][j] / T)
}
printf("\n")
}
for (i = 0 i < NUMGROUPS i++) {
for (j = 0 j < NUMGROUPS j++) {
T = gpUU[i][j] + gpUS[i][j] + gpSU[i][j]
+ gpSS[i][j]
printf("%5.4f,", (float)gpSS[i][j] / T)
}
printf("\n")
}
}
/*-------*\
| END |
\*-------*/
140

F.8 segment.c
This program reads standard input and segments the text input le into tone units using three tone unit
boundary markers. Initially this is based upon punctuation. Further research should lead to a more
rened segmentation algorithm that makes use of phrase boundaries and rules. As this program stands
punctuation is mapped into tone unit boundary symbols with the exception of quotes which are mapped
into nothing.
/******************************************************************************\
segment.c - segment text file into tone-units.
AUTHOR:
(c) Copyright July 1992, Simon Arnfield. All Rights Reserved.
SYNOPSIS:
segment
DESCRIPTION:
reads standard input and segments the text input file into tone-units
using three tone-unit boundary markers. Initially this is based upon
punctuation. Punctuation is classed either as a minor TU boundary,
a major TU bouary or nothing. Where appropriate multiple punctuation
exists one TU is produced. Precedences: || first then ^ | and none.
Distributions are taken from frequency of co-occurrence as given by
results from ttalign.
none open/close quotes
minor , ( ) hyphen
major . ! :
FILES:
REFERENCES:
BUGS:
deletes apostrophies because it can't tell them from open-quotes.
PROGRAM MODIFICATION HISTORY
Date | By | Vers | Comments
----------+-----+------+--------------------------------------------------------
3/07/92 | ScA | 1.0 | Created original code
19/07/92 | ScA | | finished version 1.0
| | |
\******************************************************************************/
/*----------------------------*\
| INCLUDES AND DEFINITIONS |
\*----------------------------*/
#include
#include
#include
#include
#define NOTALPHA 0
141

#define ALPHA 1
#define NOTU 0
#define MINORTU 1
#define MAJORTU 2
/*-------------------------*\
| FUNCTIONS DEFINITIONS |
\*-------------------------*/
void main()
{
int tu = NOTU, ch = NOTALPHA
char c, lc = '\0'
}
while (!feof(stdin)) {
c = getchar()
if (isalnum(c) || isspace(c))
ch = ALPHA
else
ch = NOTALPHA
if (c == '\'' && isalnum(lc))
ch = ALPHA
if (strstr(",()-", &c) && tu == NOTU)
tu = MINORTU
if (strstr(".!:", &c))
tu = MAJORTU
if (ch == ALPHA) {
if (tu == MINORTU)
printf("| ")
if (tu == MAJORTU)
printf("|| ")
tu = NOTU
putchar(c)
lc = c
}
}
/*-------*\
| END |
\*-------*/
F.9 probability.c
Probability is the original stress prediction model described in chapter 5. It makes no use of word
class/prosodic mark bigram frequencies but uses prosodic mark bigram frequencies.
/* probability.c copyright Simon Arnfield 15th January 1993 */
/* compile with lc -Lm -DBISTATE probability.c for bi-stress model */
/* compile with lc -Lm -DTRISTATE probability.c for tri-stress model */
#include
#include
#include
#define MAXWORDS 20
#define NUMTAGS 168
142

#define NUMBEST 1
char tags[NUMTAGS][7] ={
"&FO", "&FW", "APP$", "AT", "AT1", "BTO21",
"BTO22","CC", "CC31", "CC32", "CC33", "CCB",
"CF", "CS", "CS21", "CS22", "CSA", "CSN",
"CST", "CSW", "DA", "DA1", "DA2", "DAR",
"DAT", "DB", "DB2", "DD", "DD1", "DD121",
"DD122", "DD2","DD21", "DD22", "DD221","DD222",
"DDQ", "DDQ$","DDQV", "EX", "ICS", "IF", "II",
"II21", "II22","II31", "II32", "II33", "IO",
"IW", "JA", "JB", "JBR", "JJ", "JJR",
"JJT", "LE", "MC", "MC-MC", "MC1", "MC2",
"MD", "MF", "ND1", "NN", "NN1", "NN121",
"NN122","NN2", "NNJ", "NNJ1", "NNJ2", "NNL1",
"NNL2", "NNO", "NNO2", "NNS", "NNS1", "NNS2",
"NNSA1","NNSB1", "NNT1", "NNT2","NNU", "NNU1",
"NNU2", "NNU21", "NNU22","NP", "NP1", "NP2",
"NPD1", "NPM1","PN", "PN1", "PN121","PN122",
"PNQO", "PNQS","PP$", "PPH1", "PPHO1","PPHO2",
"PPHS1","PPHS2","PPIO1","PPIO2","PPIS1","PPIS2",
"PPX1", "PPX121","PPX122","PPX2","PPY","RA",
"REX21","REX22","RG", "RG21", "RG22", "RGA",
"RGQ", "RGQV", "RGR", "RGT", "RL", "RL21",
"RL22", "RP", "RR", "RR21", "RR22", "RR31",
"RR32", "RR33", "RRQ", "RRQV", "RRR", "RRT",
"RT", "TO", "UH", "VB0", "VBDR", "VBDZ",
"VBG", "VBM", "VBN", "VBR", "VBZ", "VD0",
"VDD", "VDG", "VDN", "VDZ", "VH0", "VHD",
"VHG", "VHN", "VHZ", "VM", "VM21", "VV0",
"VVD", "VVG", "VVN", "VVZ", "XX"}
/**** probabilities for bi-stress model ie Q A ****/
float probs[NUMTAGS][3] = {
0.0000, 0.00, 1.0000,
0.2000, 0.00, 0.8000,
0.8991, 0.00, 0.1009,
0.9605, 0.00, 0.0395,
0.9749, 0.00, 0.0250,
0.5000, 0.00, 0.5000,
0.7500, 0.00, 0.2500,
0.8712, 0.00, 0.1288,
1.0000, 0.00, 0.0000,
0.0000, 0.00, 1.0000,
1.0000, 0.00, 0.0000,
0.8284, 0.00, 0.1716,
0.5588, 0.00, 0.4412,
0.5823, 0.00, 0.4177,
0.2778, 0.00, 0.7222,
0.7778, 0.00, 0.2223,
0.7742, 0.00, 0.2258,
1.0000, 0.00, 0.0000,
0.9701, 0.00, 0.0299,
0.0714, 0.00, 0.9286,
0.3830, 0.00, 0.6170,
0.2308, 0.00, 0.7692,
0.2381, 0.00, 0.7619,
143

0.2619, 0.00, 0.7381,
0.0556, 0.00, 0.9445,
0.0889, 0.00, 0.9112,
0.1333, 0.00, 0.8666,
0.2371, 0.00, 0.7629,
0.3965, 0.00, 0.6035,
1.0000, 0.00, 0.0000,
0.0000, 0.00, 1.0000,
0.3867, 0.00, 0.6133,
1.0000, 0.00, 0.0000,
0.5000, 0.00, 0.5000,
1.0000, 0.00, 0.0000,
0.2500, 0.00, 0.7500,
0.6584, 0.00, 0.3416,
1.0000, 0.00, 0.0000,
0.0000, 0.00, 1.0000,
0.8977, 0.00, 0.1023,
0.5372, 0.00, 0.4628,
0.9478, 0.00, 0.0522,
0.8774, 0.00, 0.1226,
0.3714, 0.00, 0.6286,
0.9859, 0.00, 0.0141,
1.0000, 0.00, 0.0000,
0.0909, 0.00, 0.9091,
1.0000, 0.00, 0.0000,
0.9912, 0.00, 0.0088,
0.8626, 0.00, 0.1373,
0.0000, 0.00, 1.0000,
0.1463, 0.00, 0.8537,
0.0000, 0.00, 1.0000,
0.0852, 0.00, 0.9148,
0.0811, 0.00, 0.9189,
0.1176, 0.00, 0.8823,
0.2500, 0.00, 0.7500,
0.1119, 0.00, 0.8881,
0.2500, 0.00, 0.7500,
0.1837, 0.00, 0.8164,
0.0000, 0.00, 1.0000,
0.1908, 0.00, 0.8092,
0.3429, 0.00, 0.6572,
0.1304, 0.00, 0.8696,
0.1628, 0.00, 0.8372,
0.0759, 0.00, 0.9241,
0.0000, 0.00, 1.0000,
0.0000, 0.00, 1.0000,
0.0920, 0.00, 0.9080,
0.1624, 0.00, 0.8376,
0.0588, 0.00, 0.9412,
0.0741, 0.00, 0.9260,
0.1348, 0.00, 0.8653,
0.1111, 0.00, 0.8889,
0.3467, 0.00, 0.6534,
0.0000, 0.00, 1.0000,
0.0000, 0.00, 1.0000,
0.1413, 0.00, 0.8587,
0.0833, 0.00, 0.9167,
0.0000, 0.00, 1.0000,
0.7595, 0.00, 0.2405,
144

0.1667, 0.00, 0.8333,
0.1522, 0.00, 0.8478,
0.3000, 0.00, 0.7000,
0.1176, 0.00, 0.8824,
0.0816, 0.00, 0.9184,
0.9643, 0.00, 0.0357,
0.1852, 0.00, 0.8148,
0.5000, 0.00, 0.5000,
0.0889, 0.00, 0.9111,
0.0000, 0.00, 1.0000,
0.0000, 0.00, 1.0000,
0.0625, 0.00, 0.9376,
0.0000, 0.00, 1.0000,
0.1316, 0.00, 0.8685,
0.0000, 0.00, 1.0000,
1.0000, 0.00, 0.0000,
0.0000, 0.00, 1.0000,
0.8966, 0.00, 0.1035,
0.8333, 0.00, 0.1667,
0.9316, 0.00, 0.0684,
0.7059, 0.00, 0.2941,
0.9524, 0.00, 0.0476,
0.8788, 0.00, 0.1212,
0.8017, 0.00, 0.1984,
0.7000, 0.00, 0.3000,
1.0000, 0.00, 0.0000,
0.7797, 0.00, 0.2203,
0.9057, 0.00, 0.0944,
0.0323, 0.00, 0.9677,
0.0000, 0.00, 1.0000,
0.0000, 0.00, 1.0000,
0.1667, 0.00, 0.8333,
0.9577, 0.00, 0.0423,
0.2143, 0.00, 0.7857,
0.9677, 0.00, 0.0323,
0.0323, 0.00, 0.9678,
0.4370, 0.00, 0.5631,
1.0000, 0.00, 0.0000,
1.0000, 0.00, 0.0000,
0.1429, 0.00, 0.8571,
0.5000, 0.00, 0.5000,
0.0000, 0.00, 1.0000,
0.5263, 0.00, 0.4736,
0.5652, 0.00, 0.4348,
0.1351, 0.00, 0.8648,
1.0000, 0.00, 0.0000,
0.0000, 0.00, 1.0000,
0.3188, 0.00, 0.6811,
0.1512, 0.00, 0.8488,
0.8500, 0.00, 0.1500,
0.1000, 0.00, 0.9000,
0.6250, 0.00, 0.3750,
0.1250, 0.00, 0.8750,
0.8750, 0.00, 0.1250,
0.5694, 0.00, 0.4306,
1.0000, 0.00, 0.0000,
0.0238, 0.00, 0.9762,
0.0000, 0.00, 1.0000,
145

0.1982, 0.00, 0.8018,
0.9927, 0.00, 0.0073,
0.3077, 0.00, 0.6923,
0.9162, 0.00, 0.0838,
0.9000, 0.00, 0.1000,
0.9255, 0.00, 0.0745,
0.7857, 0.00, 0.2143,
0.0000, 0.00, 1.0000,
0.9123, 0.00, 0.0878,
0.8244, 0.00, 0.1756,
0.8545, 0.00, 0.1455,
0.2800, 0.00, 0.7200,
0.4242, 0.00, 0.5757,
0.1667, 0.00, 0.8333,
0.4000, 0.00, 0.6000,
0.3846, 0.00, 0.6154,
0.8092, 0.00, 0.1908,
0.8621, 0.00, 0.1379,
0.5455, 0.00, 0.4545,
0.3750, 0.00, 0.6250,
0.8587, 0.00, 0.1413,
0.7267, 0.00, 0.2734,
0.1429, 0.00, 0.8571,
0.1789, 0.00, 0.8211,
0.1416, 0.00, 0.8584,
0.1111, 0.00, 0.8889,
0.0812, 0.00, 0.9188,
0.2051, 0.00, 0.7948,
0.2340, 0.00, 0.7660}
float bigrams[5][5] = {
0.1016, 0.0000, 0.2397, 0.0024, 0.0115, /* QQ -- QA QT QI */
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, /* -- -- -- -- -- */
0.1305, 0.0000, 0.1442, 0.0404, 0.1377, /* AQ -- AA AT AI */
0.0226, 0.0000, 0.0202, 0.0000, 0.0000, /* TQ -- TA -- -- */
0.1005, 0.0000, 0.0488, 0.0000, 0.0000} /* IQ -- IA -- -- */
/**** end of probabilities for bi-stress model ie Q A ****/
main()
{
int i, j, k, l, pos, done, w, tustart, tuend, numberstates
double value, bigvalue[NUMBEST]
int state[MAXWORDS], bigstate[NUMBEST][MAXWORDS], stress[MAXWORDS]
float sentence[MAXWORDS][3]
char c, word[MAXWORDS][30], wordtag[MAXWORDS][7]
/* read in tu data from standard input */
if ((c = getc(stdin)) == 'T')
tustart = 3
else
tustart = 4
if (c != 'I' && tustart == 4)
printf("Invalid tustart character. Assuming I\n")
146

if ((c = getc(stdin)) == 'T')
tuend = 3
else
tuend = 4
if (c != 'I' && tuend == 4)
printf("Invalid tuend character. Assuming I\n")
scanf("%d", &w) /* get number of words */
if (w < 0 || w > 40) {
printf("Number of words(%d) too many. Exiting\n",
w)
exit(1)
}
for (i = 0 i < w i++) {
scanf("%s %s", wordtag[i], word[i])
/* find tag in tags and set sentence[w][0..2] appropriately */
for (j = 0 j < NUMTAGS j++)
if (!strcmp(tags[j], wordtag[i])) {/* found the right tag */
for (k = 0 k < 3 k++)
sentence[i][k] = probs[j][k]
break
}
if (j == NUMTAGS) {
printf("Invalid tag(%s). Exiting\n", wordtag[i])
exit(1)
}
}
/* number of possible states 3^w */
numberstates = pow(3.0, (double)w)
for (j = 0 j < NUMBEST j++)
bigvalue[j] = 0.0 /* reset best values */
for (j = 0 j < w j++)
state[j] = 0 /* set up first state */
/* print out words and tags */
for (k = 0, j = 0 j < w j++) {
k += strlen(word[j]) + strlen(wordtag[j]) + 2
#ifdef SINGLELINEOUTPUT
if (k > 70) {
printf("\n")
k = 0
}
#endif
printf("%s=%s ", word[j], wordtag[j])
/* assume unstressed, unless otherwise set below */
stress[j] = 0
for (i = 0 i < strlen(word[j]) i++)
if (word[j][i] == ',' || word[j][i] == '/'
|| word[j][i] == '`' || word[j][i] == '\\'
|| word[j][i] == '_' ||
word[j][i] == '~' || word[j][i] == '*')
stress[j] = 2
}
#ifdef SINGLELINEOUTPUT
printf("\n")
#endif
147

for (i = 0 i < numberstates i++) {
for (value = 1, j = 0 j < w j++)
value *= sentence[j][state[j]]
for (j = 1 j < w j++)
value *= bigrams[state[j-1]][state[j]]
value *= bigrams[tustart][state[0]] * bigrams[state[w-1]][tuend]
/* keep track of the top NUMBEST most probable sequences */
for (j = 0 value < bigvalue[j] && j < NUMBEST
j++)
if (j < NUMBEST) {
/* shuffle other values to make room */
for (k = NUMBEST - 1 k > j k--) {
bigvalue[k] = bigvalue[k-1]
for (l = 0 l < w l++)
bigstate[k][l] = bigstate[k-1][l]
}
bigvalue[j] = value
for (k = 0 k < w k++)
bigstate[j][k] = state[k]
}
}
pos = 0 /* update state[] to next state */
do {
if (++state[pos] == 3) {
state[pos] = 0
pos++
done = 0
} else
done = 1
} while (pos < w && !done)
for (i = 0 i < NUMBEST i++) {
printf("%g ", bigvalue[i])
if (tustart == 3)
printf("T")
else
printf("I")
k = 0
for (j = 0 j < w j++) {
if (stress[j] != bigstate[i][j])
k++
switch (bigstate[i][j]) {
case 0:
printf("Q")
break
case 1:
printf("S")
break
case 2:
printf("A")
break
}
}
148

}
}
if (tuend == 3)
printf("T ")
else
printf("I ")
if (k == 0)
printf("CORRECT\n")
else
printf("ERROR(%d)\n", k)
F.10 probability3.c
Probability3 is the original prosodic mark prediction model described in chapter 6.
/* probability.c copyright Simon Arnfield 15th January 1993 */
/* probability3.c (version 4) copyright 25/5/93, 15/7/93 */
/* cc -o probability3 probability3.c -lm */
#include
#include
#include
#define MAXWORDS 15
#define NUMTAGS 168
char tags[NUMTAGS][7] ={
"&FO", "&FW", "APP$", "AT", "AT1", "BTO21",
"BTO22","CC", "CC31", "CC32", "CC33", "CCB",
"CF", "CS", "CS21", "CS22", "CSA", "CSN",
"CST", "CSW", "DA", "DA1", "DA2", "DAR",
"DAT", "DB", "DB2", "DD", "DD1", "DD121",
"DD122", "DD2","DD21", "DD22", "DD221","DD222",
"DDQ", "DDQ$","DDQV", "EX", "ICS", "IF", "II",
"II21", "II22","II31", "II32", "II33", "IO",
"IW", "JA", "JB", "JBR", "JJ", "JJR",
"JJT", "LE", "MC", "MC-MC", "MC1", "MC2",
"MD", "MF", "ND1", "NN", "NN1", "NN121",
"NN122","NN2", "NNJ", "NNJ1", "NNJ2", "NNL1",
"NNL2", "NNO", "NNO2", "NNS", "NNS1", "NNS2",
"NNSA1","NNSB1", "NNT1", "NNT2","NNU", "NNU1",
"NNU2", "NNU21", "NNU22","NP", "NP1", "NP2",
"NPD1", "NPM1","PN", "PN1", "PN121","PN122",
"PNQO", "PNQS","PP$", "PPH1", "PPHO1","PPHO2",
"PPHS1","PPHS2","PPIO1","PPIO2","PPIS1","PPIS2",
"PPX1", "PPX121","PPX122","PPX2","PPY","RA",
"REX21","REX22","RG", "RG21", "RG22", "RGA",
"RGQ", "RGQV", "RGR", "RGT", "RL", "RL21",
"RL22", "RP", "RR", "RR21", "RR22", "RR31",
"RR32", "RR33", "RRQ", "RRQV", "RRR", "RRT",
"RT", "TO", "UH", "VB0", "VBDR", "VBDZ",
"VBG", "VBM", "VBN", "VBR", "VBZ", "VD0",
"VDD", "VDG", "VDN", "VDZ", "VH0", "VHD",
"VHG", "VHN", "VHZ", "VM", "VM21", "VV0",
"VVD", "VVG", "VVN", "VVZ", "XX"}
/**** probabilities for pent-stress model ie Rise Fall Vfallrise Str Ustr ****/
149

float probs[NUMTAGS][5] = {
0.0001, 0.0001, 0.1443, 0.0357, 0.0001,
0.1869, 0.5785, 0.2886, 0.3213, 0.1090,
0.1869, 0.7954, 1.0101, 0.5355, 6.6042,
0.0001, 1.3015, 0.4329, 2.2135, 43.9407,
0.1869, 0.0723, 0.1443, 0.5712, 16.1072,
0.0001, 0.0001, 0.0001, 0.0714, 0.0436,
0.0001, 0.0001, 0.0001, 0.0357, 0.0654,
1.1215, 0.9400, 0.8658, 2.7133, 14.8867,
0.0001, 0.0001, 0.0001, 0.0001, 0.0218,
0.0001, 0.0723, 0.0001, 0.0001, 0.0001,
0.0001, 0.0001, 0.0001, 0.0001, 0.0218,
0.3738, 0.0001, 0.1443, 0.9282, 3.0514,
0.7477, 0.0723, 0.2886, 0.2856, 0.4141,
0.7477, 0.8677, 0.0001, 1.7851, 2.0052,
0.0001, 0.2892, 0.1443, 0.2856, 0.1090,
0.0001, 0.0723, 0.1443, 0.0714, 0.3051,
0.1869, 0.2892, 0.0001, 0.5712, 1.5693,
0.0001, 0.0001, 0.0001, 0.0001, 0.5231,
0.0001, 0.0001, 0.0001, 0.2856, 5.6670,
0.1869, 0.2892, 0.0001, 0.2856, 0.0218,
0.1869, 0.2169, 0.5772, 0.7497, 0.3923,
0.1869, 0.1446, 0.2886, 0.5355, 0.1308,
0.5607, 0.4338, 1.0101, 0.5712, 0.2180,
0.3738, 0.6508, 0.4329, 0.6069, 0.2398,
0.3738, 0.1446, 1.2987, 0.1428, 0.0218,
0.1869, 1.9523, 2.3088, 1.3567, 0.1744,
0.0001, 0.0723, 0.8658, 0.2142, 0.0436,
0.0001, 1.0123, 2.4531, 1.5352, 0.5013,
1.1215, 3.0369, 4.6176, 3.2845, 2.4629,
0.0001, 0.0001, 0.0001, 0.0001, 0.0654,
0.0001, 0.0723, 0.1443, 0.0357, 0.0001,
0.9346, 0.2892, 0.5772, 1.1782, 0.6321,
0.0001, 0.0001, 0.0001, 0.0001, 0.0872,
0.0001, 0.0723, 0.0001, 0.0357, 0.0436,
0.0001, 0.0001, 0.0001, 0.0001, 0.2616,
0.0001, 0.2169, 0.1443, 0.1785, 0.0654,
0.5607, 0.7954, 0.1443, 1.4281, 2.3104,
0.0001, 0.0001, 0.0001, 0.0001, 0.0654,
0.0001, 0.2169, 0.1443, 0.0001, 0.0001,
0.0001, 0.0001, 0.0001, 0.3213, 1.7219,
0.1869, 0.7231, 0.7215, 1.4281, 1.4167,
0.1869, 0.0723, 0.0001, 0.4284, 5.5362,
1.4953, 2.6030, 3.7518, 6.7119, 40.2572,
0.1869, 0.6508, 1.0101, 0.9639, 0.5667,
0.0001, 0.0001, 0.0001, 0.0357, 1.5257,
0.0001, 0.0001, 0.0001, 0.0001, 0.2398,
0.0001, 0.2169, 0.1443, 0.2142, 0.0218,
0.0001, 0.0001, 0.0001, 0.0001, 0.2398,
0.0001, 0.0723, 0.0001, 0.2499, 19.5292,
0.0001, 0.2892, 0.4329, 0.6426, 3.4220,
0.1869, 0.2892, 0.0001, 0.0714, 0.0001,
1.3084, 1.8800, 3.0303, 3.0703, 0.5231,
0.0001, 0.0001, 0.1443, 0.0357, 0.0001,
15.5140, 25.3073, 30.8802, 32.4884, 3.1604,
0.3738, 0.5061, 1.7316, 0.4641, 0.0654,
0.3738, 1.0123, 2.0202, 0.5355, 0.1308,
0.0001, 0.2169, 0.1443, 0.2856, 0.0872,
150

2.6168, 7.2307, 5.6277, 8.7112, 1.0898,
0.0001, 0.0723, 0.0001, 0.0714, 0.0218,
0.9346, 0.8677, 1.7316, 1.8208, 0.3923,
0.5607, 0.2892, 0.2886, 0.0714, 0.0001,
0.5607, 1.5907, 4.1847, 2.4634, 0.6321,
0.5607, 1.0846, 0.1443, 0.9639, 0.5231,
0.1869, 0.6508, 0.1443, 0.3213, 0.0654,
3.1776, 2.6030, 1.2987, 2.9275, 0.6103,
85.7944, 71.0774, 67.0996, 51.5173, 5.9939,
0.0001, 0.0723, 0.0001, 0.0357, 0.0001,
0.0001, 0.0723, 0.0001, 0.0357, 0.0001,
34.7664, 29.5011, 24.3867, 22.4206, 3.0732,
2.2430, 1.8800, 2.1645, 1.6066, 0.4141,
0.5607, 0.3615, 0.1443, 0.2499, 0.0218,
0.5607, 0.3615, 0.4329, 0.4998, 0.0436,
3.5514, 3.1092, 1.4430, 1.7851, 0.4141,
0.0001, 0.3615, 0.1443, 0.3570, 0.0436,
0.5607, 0.7954, 1.4430, 0.8925, 0.5667,
0.0001, 0.2169, 0.0001, 0.0357, 0.0001,
0.1869, 0.0001, 0.0001, 0.0001, 0.0001,
1.1215, 0.7954, 1.0101, 1.9636, 0.2833,
0.3738, 0.2169, 0.5772, 0.0714, 0.0218,
0.1869, 0.0001, 0.0001, 0.0001, 0.0001,
0.3738, 0.0723, 0.0001, 0.5712, 1.3078,
3.3645, 3.6876, 2.3088, 3.2131, 0.7629,
2.4299, 2.4584, 1.1544, 0.8211, 0.3051,
0.1869, 0.0723, 0.1443, 0.1428, 0.0654,
0.7477, 0.2892, 0.4329, 0.1428, 0.0436,
0.5607, 1.5907, 0.5772, 0.5712, 0.0872,
0.0001, 0.0001, 0.0001, 0.0357, 0.5885,
0.0001, 0.7231, 0.4329, 0.3213, 0.1090,
0.0001, 0.0723, 0.0001, 0.1071, 0.0872,
26.7290, 23.1381, 27.7056, 19.4216, 2.5501,
0.0001, 0.1446, 0.0001, 0.0001, 0.0001,
0.0001, 0.3615, 0.0001, 0.1428, 0.0001,
0.1869, 1.1569, 1.1544, 0.1785, 0.0436,
0.0001, 0.2169, 0.0001, 0.0357, 0.0001,
0.7477, 0.5061, 1.1544, 0.4998, 0.1090,
0.0001, 0.0001, 0.1443, 0.0357, 0.0001,
0.0001, 0.0001, 0.0001, 0.0001, 0.0436,
0.0001, 0.0001, 0.0001, 0.0357, 0.0001,
0.1869, 0.0723, 0.0001, 0.2499, 1.7001,
0.0001, 0.0723, 0.0001, 0.0001, 0.1090,
0.0001, 0.1446, 0.0001, 0.5712, 5.3400,
0.1869, 0.0723, 0.0001, 0.1071, 0.2616,
0.0001, 0.0001, 0.1443, 0.0357, 0.8718,
0.0001, 0.4338, 0.1443, 0.3213, 2.5283,
0.1869, 0.3615, 1.0101, 0.3927, 2.1142,
0.0001, 0.0723, 0.0001, 0.0714, 0.1526,
0.0001, 0.0001, 0.0001, 0.0001, 0.5013,
0.0001, 0.0723, 0.4329, 0.3213, 1.0026,
0.0001, 0.0001, 0.1443, 0.3213, 2.0924,
0.1869, 0.7954, 1.1544, 0.3570, 0.0218,
0.0001, 0.0001, 0.0001, 0.0714, 0.0001,
0.0001, 0.0723, 0.0001, 0.0357, 0.0001,
0.1869, 0.0723, 0.7215, 0.1071, 0.0436,
0.0001, 0.0001, 0.1443, 0.0714, 1.4821,
0.3738, 0.5061, 0.4329, 0.3570, 0.1308,
151

0.0001, 0.0001, 0.0001, 0.0357, 0.6539,
0.7477, 0.1446, 0.1443, 0.8211, 0.0218,
0.1869, 1.0123, 0.7215, 1.6780, 1.1334,
0.0001, 0.0001, 0.0001, 0.0001, 0.0436,
0.0001, 0.0001, 0.0001, 0.0001, 0.0436,
0.0001, 0.2169, 0.0001, 0.1071, 0.0218,
0.1869, 0.0001, 0.0001, 0.1071, 0.0872,
0.0001, 0.0001, 0.0001, 0.0357, 0.0001,
0.0001, 0.0723, 0.4329, 0.4998, 0.4359,
0.1869, 0.0723, 0.1443, 0.2499, 0.2833,
1.6822, 2.0969, 1.0101, 1.8208, 0.3269,
0.0001, 0.0001, 0.0001, 0.0001, 0.0654,
0.1869, 0.0723, 0.1443, 0.0001, 0.0001,
3.3645, 4.1215, 1.8759, 1.8922, 1.4385,
8.5981, 13.3767, 14.2857, 11.6744, 2.5501,
0.0001, 0.0001, 0.0001, 0.3213, 1.1116,
1.4953, 1.2292, 0.8658, 0.8211, 0.1308,
0.0001, 0.0001, 0.0001, 0.1071, 0.1090,
0.0001, 0.4338, 0.0001, 0.0357, 0.0218,
0.0001, 0.0001, 0.0001, 0.0357, 0.1526,
0.0001, 0.7954, 0.2886, 0.6426, 0.8936,
0.0001, 0.0001, 0.0001, 0.0001, 0.0218,
0.5607, 0.7231, 1.0101, 0.7497, 0.0218,
0.0001, 0.0001, 0.0001, 0.0357, 0.0001,
2.6168, 1.3015, 1.7316, 1.6066, 0.4795,
0.0001, 0.0001, 0.0001, 0.1071, 8.9364,
0.9346, 0.2169, 0.0001, 0.3570, 0.1744,
0.1869, 0.2169, 0.0001, 0.3570, 3.3348,
0.0001, 0.1446, 0.1443, 0.2499, 1.9616,
0.0001, 0.1446, 0.1443, 0.6426, 5.6888,
0.0001, 0.0001, 0.0001, 0.2142, 0.4795,
0.0001, 0.0723, 0.0001, 0.0001, 0.0001,
0.1869, 0.0723, 0.0001, 0.2856, 2.2668,
0.0001, 0.6508, 0.2886, 0.4284, 2.3540,
0.5607, 0.7954, 1.1544, 0.6069, 4.9913,
0.0001, 0.4338, 1.1544, 0.1428, 0.1526,
0.1869, 0.4338, 0.0001, 0.4284, 0.3051,
0.0001, 0.0001, 0.1443, 0.1428, 0.0218,
0.0001, 0.0723, 0.0001, 0.0714, 0.0436,
0.0001, 0.1446, 0.0001, 0.2142, 0.1090,
0.1869, 0.2169, 0.7215, 0.7140, 2.6809,
0.0001, 0.1446, 0.0001, 0.3570, 1.6347,
0.0001, 0.0001, 0.0001, 0.1785, 0.1308,
0.0001, 0.1446, 0.0001, 0.1071, 0.0654,
0.1869, 0.2169, 0.1443, 0.2856, 1.7219,
0.3738, 1.8077, 1.4430, 1.6066, 4.7515,
0.0001, 0.1446, 0.0001, 0.1428, 0.0218,
13.8318, 10.9183, 8.0808, 13.5666, 3.1386,
4.2991, 5.3507, 4.3290, 9.9607, 1.4603,
4.4860, 4.3384, 3.6075, 8.1042, 0.9154,
14.0187, 12.2198, 10.3896, 12.9597, 1.3078,
2.4299, 2.0969, 0.7215, 3.8558, 0.8718,
0.1869, 1.8077, 1.1544, 1.3567, 0.4795 }
float bigram[6][6] = {
0.0000, 0.0040, 0.0148, 0.0047, 0.0372, 0.1060, /* TUB */
0.1313, 0.0022, 0.0025, 0.0013, 0.0082, 0.0212, /* rise */
152

0.0903, 0.0008, 0.0057, 0.0040, 0.0200, 0.0458, /* fall */
0.0685, 0.0007, 0.0043, 0.0015, 0.0413, 0.0503, /* V fallrise */
0.0405, 0.0102, 0.0257, 0.0072, 0.0262, 0.0570, /* stress */
0.0095, 0.0080, 0.0318, 0.0098, 0.0530, 0.0545} /* unstress */
/***
/* 0.000, 0.024, 0.089, 0.028, 0.223, 0.636, /* TUB */
/* 0.788, 0.013, 0.015, 0.008, 0.049, 0.127, /* Rise */
/* 0.542, 0.005, 0.034, 0.024, 0.120, 0.275, /* Fall */
/* 0.411, 0.004, 0.026, 0.009, 0.248, 0.302, /* V fallrise */
/* 0.243, 0.061, 0.154, 0.043, 0.157, 0.342, /* Stress */
/* 0.057, 0.048, 0.191, 0.059, 0.318, 0.327} /* Unstress */
/* TUB Rise Fall V Str Ustr */
main()
{
int i, j, k, pos, done, w, numberstates, s, tub = 0
double value, bigvalue, v1, v2
short int state[MAXWORDS], bigstate[MAXWORDS]
float sentence[MAXWORDS][6]
char wordtag[MAXWORDS][7], prosody[MAXWORDS][24]
/**printf("\nNumber of tags >")**/
scanf("%d", &w) /* get number of words */
if (w < 0 || w > MAXWORDS) {
/*printf("Number of words(%d) not suitable. Exiting\n",w)*/
exit(0)
}
for (j = 0 j < w j++)
state[j] = 1 /* set up first state range from 1...1 to 5...5 */
bigvalue = 0.0 /* reset best values */
/**printf("Expecting %d tags (and or tone unit boundaries)\n",w)**/
for (i = 0 i < w i++) {
/**printf("Tag & word %d(of %d)>",i+1,w)**/
scanf("%s %s", wordtag[i], prosody[i])
for (j = 0 j < NUMTAGS j++)
if (!strcmp(tags[j], wordtag[i])) {/* found the right tag */
sentence[i][0] = 0.0 /* TUB */
sentence[i][1] = probs[j][0] /* RISE */
sentence[i][2] = probs[j][1] /* FALL */
sentence[i][3] = probs[j][2] /* FALL-RISE */
sentence[i][4] = probs[j][3] /* STRESSED */
sentence[i][5] = probs[j][4] /* UNSTRESSED */
break
}
if (!strcmp("{CP}", wordtag[i])) {/* ie it is a compound */
/*printf("A Compound - assuming probably stressed\n")*/
sentence[i][0] = 0.0
sentence[i][1] = 0.25 /* treat as alomst */
sentence[i][2] = 0.25
sentence[i][3] = 0.1
sentence[i][4] = 0.399 /* always stressed */
sentence[i][5] = 0.001
153

}
}
if (j == NUMTAGS) {
/*printf("Assuming %s is a TUB - restricting search space\n",
prosody[i])*/
tub++
sentence[i][0] = 1.0 /* a tu is always a tu */
sentence[i][1] = 0.0
sentence[i][2] = 0.0
sentence[i][3] = 0.0
sentence[i][4] = 0.0
sentence[i][5] = 0.0
state[i] = 0 /* prevent state changing a tu */
}
/* number of possible states 5^w */
numberstates = pow(5.0, (double)(w - tub))
/* but TUBs don't alter hence reduced search space */
/**printf("Processing %d states\n",numberstates)**/
for (i = 0 i < numberstates i++) {
value = 1.0
v1 = 1.0
v2 = 1.0
for (j = 0 j < w j++)
v1 *= (double)sentence[j][state[j]]
for (j = 1 j < w j++)
v2 *= (double)bigram[state[j-1]][state[j]]
value = v1 * v2
if (value > bigvalue) {
/** printf("BEST SO FAR:")**/
bigvalue = value
for (k = 0 k < w k++) {
bigstate[k] = state[k] /* save new best state */
/** switch(state[k]) {
case 0: printf("|") break
case 1: printf("R") break
case 2: printf("F") break
case 3: printf("V") break
case 4: printf("S") break
case 5: printf("U")
}**/
}
/** printf(" %e\n",value)**/
}
pos = w - 1 /* update state[] to next state */
do {
done = 1 /* by default have done unless changed below */
if (state[pos] == 0)
pos-- /* don't change tub state */
if (pos >= 0) { /* don't go past end */
if (++state[pos] == 6) {/* if state increases to 6 */
state[pos] = 1 /* reset to 1 */
154

}
pos-- /* and point to next word */
done = 0 /* and say we haven't done */
}
}
} while (pos >= 0 && !done)
for (i = 0 i < w i++)
printf("%s=%s ", wordtag[i], prosody[i])
printf("\nPredicted: ")
for (i = 0 i < w i++) {
switch (bigstate[i]) {
case 0:
printf("|")
break
case 1:
printf("R")
break
case 2:
printf("F")
break
case 3:
printf("V")
break
case 4:
printf("S")
break
case 5:
printf("U")
}
}
printf("\nShould Be: ")
for (i = 0 i < w i++) {
if (bigstate[i] == 0)
s = 0 /* tu boundary */
else
for (s = 5, j = 0 j < strlen(prosody[i])
j++) {
if ((prosody[i][j] == ',' || prosody[i][j]
== '/') && s >= 4)
s = 1
if ((prosody[i][j] == '\\' || prosody[i][j]
== '`') && s >= 4)
s = 2
if ((prosody[i][j] == ',' || prosody[i][j]
== '/') && s == 2)
s = 3
if ((prosody[i][j] == '*' || prosody[i][j]
== '_' || prosody[i][j] == '~') &&
s == 5)
s = 4
if ((prosody[i][j] == '') && s == 5)
s = 4
}
switch (s) {
case 0:
155

}
printf("|")
break
case 1:
printf("R")
break
case 2:
printf("F")
break
case 3:
printf("V")
break
case 4:
printf("S")
break
case 5:
printf("U")
}
}
printf("\n")
F.11 probabilityc.c
Probabilityc is the composite model for prosodic mark prediction. See chapters 5 and 6.
/* probabilityc.c copyright Simon Arnfield 15th January 1993 */
/* multistate-probabilities.c (version 4) copyright 25/5/93 */
/* probability3.c (version 4) copyright 25/5/93, 15/7/93 */
/* probabilityc.c (composite model) copyright 2/8/93 */
/* cc -o probabilityc probabilityc.c -lm */
#include
#include
#include
#define MAXWORDS 20
#define NUMTAGS 187
#define NUMBEST 2
struct tagtype {
char tagname[7]
int group
float prb2U, prb2S
float prb5R, prb5F, prb5V, prb5S, prb5U
}
struct tagtype tags[187] = {
"&FO", 6, 0.0025, 0.9975, 0.0001, 0.0001,
0.1443, 0.0357, 0.0001,
"&FW", 1, 0.2000, 0.8000, 0.1869, 0.5785,
0.2886, 0.3213, 0.1090, /* not sure about group */
"APP$", 1, 0.8991, 0.1009, 0.1869, 0.7954,
1.0101, 0.5355, 6.6042,
"AT", 3, 0.9605, 0.0395, 0.0001, 1.3015,
0.4329, 2.2135, 43.9407,
"AT1", 3, 0.9749, 0.0250, 0.1869, 0.0723,
0.1443, 0.5712, 16.1072,
156

"BTO", 3, 0.5000, 0.5000, 0.0001, 0.0001,
0.0001, 0.1000, 0.1000, /* no data */
"BTO21", 3, 0.5000, 0.5000, 0.0001, 0.0001,
0.0001, 0.0714, 0.0436,
"BTO22", 3, 0.7500, 0.2500, 0.0001, 0.0001,
0.0001, 0.0357, 0.0654,
"CC", 1, 0.8712, 0.1288, 1.1215, 0.9400,
0.8658, 2.7133, 14.8867,
"CC31", 1, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0218,
"CC32", 1, 0.0025, 0.9975, 0.0001, 0.0723,
0.0001, 0.0001, 0.0001,
"CC33", 1, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0218,
"CCB", 4, 0.8284, 0.1716, 0.3738, 0.0001,
0.1443, 0.9282, 3.0514,
"CF", 4, 0.5588, 0.4412, 0.7477, 0.0723,
0.2886, 0.2856, 0.4141,
"CS", 4, 0.5823, 0.4177, 0.7477, 0.8677,
0.0001, 1.7851, 2.0052,
"CS21", 4, 0.2778, 0.7222, 0.0001, 0.2892,
0.1443, 0.2856, 0.1090,
"CS22", 4, 0.7778, 0.2223, 0.0001, 0.0723,
0.1443, 0.0714, 0.3051,
"CSA", 4, 0.7742, 0.2258, 0.1869, 0.2892,
0.0001, 0.5712, 1.5693,
"CSN", 3, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.5231,
"CST", 3, 0.9701, 0.0299, 0.0001, 0.0001,
0.0001, 0.2856, 5.6670,
"CSW", 4, 0.0714, 0.9286, 0.1869, 0.2892,
0.0001, 0.2856, 0.0218,
"DA", 5, 0.3830, 0.6170, 0.1869, 0.2169,
0.5772, 0.7497, 0.3923,
"DA1", 5, 0.2308, 0.7692, 0.1869, 0.1446,
0.2886, 0.5355, 0.1308,
"DA2", 5, 0.2381, 0.7619, 0.5607, 0.4338,
1.0101, 0.5712, 0.2180,
"DA2R", 5, 0.5000, 0.5000, 0.2000, 0.2000,
0.2000, 0.2000, 0.2000, /* no data */
"DAR", 5, 0.2619, 0.7381, 0.3738, 0.6508,
0.4329, 0.6069, 0.2398,
"DAT", 5, 0.0556, 0.9445, 0.3738, 0.1446,
1.2987, 0.1428, 0.0218,
"DB", 5, 0.0889, 0.9112, 0.1869, 1.9523,
2.3088, 1.3567, 0.1744,
"DB2", 5, 0.1333, 0.8666, 0.0001, 0.0723,
0.8658, 0.2142, 0.0436,
"DD", 6, 0.2371, 0.7629, 0.0001, 1.0123,
2.4531, 1.5352, 0.5013,
"DD1", 9, 0.3965, 0.6035, 1.1215, 3.0369,
4.6176, 3.2845, 2.4629,
"DD121", 9, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0654,
"DD122", 9, 0.0025, 0.9975, 0.0001, 0.0723,
0.1443, 0.0357, 0.0001,
"DD2", 9, 0.3867, 0.6133, 0.9346, 0.2892,
0.5772, 1.1782, 0.6321,
157

"DD21", 9, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0872,
"DD22", 9, 0.5000, 0.5000, 0.0001, 0.0723,
0.0001, 0.0357, 0.0436,
"DD221", 9, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.2616,
"DD222", 9, 0.2500, 0.7500, 0.0001, 0.2169,
0.1443, 0.1785, 0.0654,
"DDQ", 4, 0.6584, 0.3416, 0.5607, 0.7954,
0.1443, 1.4281, 2.3104,
"DDQ$", 4, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0654,
"DDQV", 4, 0.0025, 0.9975, 0.0001, 0.2169,
0.1443, 0.0001, 0.0001,
"EX", 2, 0.8977, 0.1023, 0.0001, 0.0001,
0.0001, 0.3213, 1.7219,
"ICS", 4, 0.5372, 0.4628, 0.1869, 0.7231,
0.7215, 1.4281, 1.4167,
"IF", 2, 0.9478, 0.0522, 0.1869, 0.0723,
0.0001, 0.4284, 5.5362,
"II", 1, 0.8774, 0.1226, 1.4953, 2.6030,
3.7518, 6.7119, 40.2572,
"II21", 9, 0.3714, 0.6286, 0.1869, 0.6508,
1.0101, 0.9639, 0.5667,
"II22", 3, 0.9859, 0.0141, 0.0001, 0.0001,
0.0001, 0.0357, 1.5257,
"II31", 3, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.2398,
"II32", 9, 0.0909, 0.9091, 0.0001, 0.2169,
0.1443, 0.2142, 0.0218,
"II33", 3, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.2398,
"IO", 3, 0.9912, 0.0088, 0.0001, 0.0723,
0.0001, 0.2499, 19.5292,
"IW", 1, 0.8626, 0.1373, 0.0001, 0.2892,
0.4329, 0.6426, 3.4220,
"JA", 5, 0.0025, 0.9975, 0.1869, 0.2892,
0.0001, 0.0714, 0.0001,
"JB", 5, 0.1463, 0.8537, 1.3084, 1.8800,
3.0303, 3.0703, 0.5231,
"JBR", 5, 0.0025, 0.9975, 0.0001, 0.0001,
0.1443, 0.0357, 0.0001,
"JJ", 5, 0.0852, 0.9148, 15.5140, 25.3073,
30.8802, 32.4884, 3.1604,
"JJR", 5, 0.0811, 0.9189, 0.3738, 0.5061,
1.7316, 0.4641, 0.0654,
"JJT", 5, 0.1176, 0.8823, 0.3738, 1.0123,
2.0202, 0.5355, 0.1308,
"LE", 4, 0.2500, 0.7500, 0.0001, 0.2169,
0.1443, 0.2856, 0.0872,
"MC", 6, 0.1119, 0.8881, 2.6168, 7.2307,
5.6277, 8.7112, 1.0898,
"MC-MC", 6, 0.2500, 0.7500, 0.0001, 0.0723,
0.0001, 0.0714, 0.0218,
"MC1", 6, 0.1837, 0.8164, 0.9346, 0.8677,
1.7316, 1.8208, 0.3923,
"MC2", 6, 0.0025, 0.9975, 0.5607, 0.2892,
0.2886, 0.0714, 0.0001,
158

"MD", 6, 0.1908, 0.8092, 0.5607, 1.5907,
4.1847, 2.4634, 0.6321,
"MF", 9, 0.3429, 0.6572, 0.5607, 1.0846,
0.1443, 0.9639, 0.5231,
"ND1", 8, 0.1304, 0.8696, 0.1869, 0.6508,
0.1443, 0.3213, 0.0654,
"NN", 5, 0.1628, 0.8372, 3.1776, 2.6030,
1.2987, 2.9275, 0.6103,
"NN1", 8, 0.0759, 0.9241, 85.7944, 71.0774,
67.0996, 51.5173, 5.9939,
"NN121", 8, 0.0025, 0.9975, 0.0001, 0.0723,
0.0001, 0.0357, 0.0001,
"NN122", 8, 0.0025, 0.9975, 0.0001, 0.0723,
0.0001, 0.0357, 0.0001,
"NN2", 8, 0.0920, 0.9080, 34.7664, 29.5011,
24.3867, 22.4206, 3.0732,
"NNJ", 8, 0.1624, 0.8376, 2.2430, 1.8800,
2.1645, 1.6066, 0.4141,
"NNJ1", 8, 0.0588, 0.9412, 0.5607, 0.3615,
0.1443, 0.2499, 0.0218,
"NNJ2", 8, 0.0741, 0.9260, 0.5607, 0.3615,
0.4329, 0.4998, 0.0436,
"NNL1", 8, 0.1348, 0.8653, 3.5514, 3.1092,
1.4430, 1.7851, 0.4141,
"NNL2", 8, 0.1111, 0.8889, 0.0001, 0.3615,
0.1443, 0.3570, 0.0436,
"NNO", 9, 0.3467, 0.6534, 0.5607, 0.7954,
1.4430, 0.8925, 0.5667,
"NNO2", 9, 0.0025, 0.9975, 0.0001, 0.2169,
0.0001, 0.0357, 0.0001,
"NNS", 7, 0.0025, 0.9975, 0.1869, 0.0001,
0.0001, 0.0001, 0.0001,
"NNS1", 7, 0.1413, 0.8587, 1.1215, 0.7954,
1.0101, 1.9636, 0.2833,
"NNS2", 7, 0.0833, 0.9167, 0.3738, 0.2169,
0.5772, 0.0714, 0.0218,
"NNSA1", 4, 0.0025, 0.9975, 0.1869, 0.0001,
0.0001, 0.0001, 0.0001,
"NNSB1", 4, 0.7595, 0.2405, 0.3738, 0.0723,
0.0001, 0.5712, 1.3078,
"NNT1", 8, 0.1667, 0.8333, 3.3645, 3.6876,
2.3088, 3.2131, 0.7629,
"NNT2", 8, 0.1522, 0.8478, 2.4299, 2.4584,
1.1544, 0.8211, 0.3051,
"NNU", 9, 0.3000, 0.7000, 0.1869, 0.0723,
0.1443, 0.1428, 0.0654,
"NNU1", 9, 0.1176, 0.8824, 0.7477, 0.2892,
0.4329, 0.1428, 0.0436,
"NNU2", 9, 0.0816, 0.9184, 0.5607, 1.5907,
0.5772, 0.5712, 0.0872,
"NNU21", 9, 0.9643, 0.0357, 0.0001, 0.0001,
0.0001, 0.0357, 0.5885,
"NNU22", 9, 0.1852, 0.8148, 0.0001, 0.7231,
0.4329, 0.3213, 0.1090,
"NP", 5, 0.5000, 0.5000, 0.0001, 0.0723,
0.0001, 0.1071, 0.0872,
"NP1", 5, 0.0889, 0.9111, 26.7290, 23.1381,
27.7056, 19.4216, 2.5501,
159

"NP2", 5, 0.0025, 0.9975, 0.0001, 0.1446,
0.0001, 0.0001, 0.0001,
"NPD1", 9, 0.0025, 0.9975, 0.0001, 0.3615,
0.0001, 0.1428, 0.0001,
"NPM1", 9, 0.0625, 0.9376, 0.1869, 1.1569,
1.1544, 0.1785, 0.0436,
"PN", 1, 0.0025, 0.9975, 0.0001, 0.2169,
0.0001, 0.0357, 0.0001, /* not sure about group */
"PN1", 1, 0.1316, 0.8685, 0.7477, 0.5061,
1.1544, 0.4998, 0.1090, /* not sure about group */
"PN121", 1, 0.0025, 0.9975, 0.0001, 0.0001,
0.1443, 0.0357, 0.0001, /* not sure about group */
"PN122", 1, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0436, /* not sure about group */
"PNQO", 2, 0.0025, 0.9975, 0.0001, 0.0001,
0.0001, 0.0357, 0.0001,
"PNQS", 2, 0.8966, 0.1035, 0.1869, 0.0723,
0.0001, 0.2499, 1.7001,
"PP$", 1, 0.8333, 0.1667, 0.0001, 0.0723,
0.0001, 0.0001, 0.1090,
"PPH1", 2, 0.9316, 0.0684, 0.0001, 0.1446,
0.0001, 0.5712, 5.3400,
"PPHO1", 1, 0.7059, 0.2941, 0.1869, 0.0723,
0.0001, 0.1071, 0.2616,
"PPHO2", 4, 0.9524, 0.0476, 0.0001, 0.0001,
0.1443, 0.0357, 0.8718,
"PPHS1", 1, 0.8788, 0.1212, 0.0001, 0.4338,
0.1443, 0.3213, 2.5283,
"PPHS2", 4, 0.8017, 0.1984, 0.1869, 0.3615,
1.0101, 0.3927, 2.1142,
"PPIO1", 2, 0.7000, 0.3000, 0.0001, 0.0723,
0.0001, 0.0714, 0.1526,
"PPIO2", 2, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.5013,
"PPIS1", 2, 0.7797, 0.2203, 0.0001, 0.0723,
0.4329, 0.3213, 1.0026,
"PPIS2", 2, 0.9057, 0.0944, 0.0001, 0.0001,
0.1443, 0.3213, 2.0924,
"PPX1", 8, 0.0323, 0.9677, 0.1869, 0.7954,
1.1544, 0.3570, 0.0218,
"PPX121", 8, 0.0025, 0.9975, 0.0001, 0.0001,
0.0001, 0.0714, 0.0001,
"PPX122", 8, 0.0025, 0.9975, 0.0001, 0.0723,
0.0001, 0.0357, 0.0001,
"PPX2", 8, 0.1667, 0.8333, 0.1869, 0.0723,
0.7215, 0.1071, 0.0436,
"PPX221", 8, 0.5000, 0.5000, 0.2000, 0.2000,
0.2000, 0.2000, 0.2000, /* no data */
"PPX222", 8, 0.5000, 0.5000, 0.2000, 0.2000,
0.2000, 0.2000, 0.2000, /* no data */
"PPY", 3, 0.9577, 0.0423, 0.0001, 0.0001,
0.1443, 0.0714, 1.4821,
"RA", 5, 0.2143, 0.7857, 0.3738, 0.5061,
0.4329, 0.3570, 0.1308,
"REX", 9, 0.5000, 0.5000, 0.2000, 0.2000,
0.2000, 0.2000, 0.2000, /* no data */
"REX21", 9, 0.9677, 0.0323, 0.0001, 0.0001,
0.0001, 0.0357, 0.6539,
160

"REX22", 9, 0.0323, 0.9678, 0.7477, 0.1446,
0.1443, 0.8211, 0.0218,
"RG", 9, 0.4370, 0.5631, 0.1869, 1.0123,
0.7215, 1.6780, 1.1334,
"RG21", 9, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0436,
"RG22", 9, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0436,
"RGA", 9, 0.1429, 0.8571, 0.0001, 0.2169,
0.0001, 0.1071, 0.0218,
"RGQ", 9, 0.5000, 0.5000, 0.1869, 0.0001,
0.0001, 0.1071, 0.0872,
"RGQV", 9, 0.0025, 0.9975, 0.0001, 0.0001,
0.0001, 0.0357, 0.0001,
"RGR", 9, 0.5263, 0.4736, 0.0001, 0.0723,
0.4329, 0.4998, 0.4359,
"RGT", 9, 0.5652, 0.4348, 0.1869, 0.0723,
0.1443, 0.2499, 0.2833,
"RL", 5, 0.1351, 0.8648, 1.6822, 2.0969,
1.0101, 1.8208, 0.3269,
"RL21", 5, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0654,
"RL22", 5, 0.0025, 0.9975, 0.1869, 0.0723,
0.1443, 0.0001, 0.0001,
"RP", 9, 0.3188, 0.6811, 3.3645, 4.1215,
1.8759, 1.8922, 1.4385,
"RR", 5, 0.1512, 0.8488, 8.5981, 13.3767,
14.2857, 11.6744, 2.5501,
"RR21", 5, 0.8500, 0.1500, 0.0001, 0.0001,
0.0001, 0.3213, 1.1116,
"RR22", 5, 0.1000, 0.9000, 1.4953, 1.2292,
0.8658, 0.8211, 0.1308,
"RR31", 5, 0.6250, 0.3750, 0.0001, 0.0001,
0.0001, 0.1071, 0.1090,
"RR32", 5, 0.1250, 0.8750, 0.0001, 0.4338,
0.0001, 0.0357, 0.0218,
"RR33", 5, 0.8750, 0.1250, 0.0001, 0.0001,
0.0001, 0.0357, 0.1526,
"RRQ", 5, 0.5694, 0.4306, 0.0001, 0.7954,
0.2886, 0.6426, 0.8936,
"RRQV", 5, 0.9975, 0.0025, 0.0001, 0.0001,
0.0001, 0.0001, 0.0218,
"RRR", 5, 0.0238, 0.9762, 0.5607, 0.7231,
1.0101, 0.7497, 0.0218,
"RRT", 5, 0.0025, 0.9975, 0.0001, 0.0001,
0.0001, 0.0357, 0.0001,
"RT", 5, 0.1982, 0.8018, 2.6168, 1.3015,
1.7316, 1.6066, 0.4795,
"TO", 3, 0.9927, 0.0073, 0.0001, 0.0001,
0.0001, 0.1071, 8.9364,
"UH", 1, 0.3077, 0.6923, 0.9346, 0.2169,
0.0001, 0.3570, 0.1744, /* not sure about group */
"VB0", 2, 0.9162, 0.0838, 0.1869, 0.2169,
0.0001, 0.3570, 3.3348,
"VBDR", 1, 0.9000, 0.1000, 0.0001, 0.1446,
0.1443, 0.2499, 1.9616,
"VBDZ", 2, 0.9255, 0.0745, 0.0001, 0.1446,
0.1443, 0.6426, 5.6888,
161

"VBG", 1, 0.7857, 0.2143, 0.0001, 0.0001,
0.0001, 0.2142, 0.4795,
"VBM", 1, 0.0025, 0.9975, 0.0001, 0.0723,
0.0001, 0.0001, 0.0001,
"VBN", 2, 0.9123, 0.0878, 0.1869, 0.0723,
0.0001, 0.2856, 2.2668,
"VBR", 4, 0.8244, 0.1756, 0.0001, 0.6508,
0.2886, 0.4284, 2.3540,
"VBZ", 1, 0.8545, 0.1455, 0.5607, 0.7954,
1.1544, 0.6069, 4.9913,
"VD0", 1, 0.2800, 0.7200, 0.0001, 0.4338,
1.1544, 0.1428, 0.1526, /* maybe should be in group 2 */
"VDD", 1, 0.4242, 0.5757, 0.1869, 0.4338,
0.0001, 0.4284, 0.3051,
"VDG", 1, 0.1667, 0.8333, 0.0001, 0.0001,
0.1443, 0.1428, 0.0218,
"VDN", 1, 0.4000, 0.6000, 0.0001, 0.0723,
0.0001, 0.0714, 0.0436, /* maybe should be in group 2 */
"VDZ", 1, 0.3846, 0.6154, 0.0001, 0.1446,
0.0001, 0.2142, 0.1090,
"VH0", 4, 0.8092, 0.1908, 0.1869, 0.2169,
0.7215, 0.7140, 2.6809,
"VHD", 1, 0.8621, 0.1379, 0.0001, 0.1446,
0.0001, 0.3570, 1.6347,
"VHG", 1, 0.5455, 0.4545, 0.0001, 0.0001,
0.0001, 0.1785, 0.1308,
"VHN", 1, 0.3750, 0.6250, 0.0001, 0.1446,
0.0001, 0.1071, 0.0654, /* maybe should be in group 2 */
"VHZ", 1, 0.8587, 0.1413, 0.1869, 0.2169,
0.1443, 0.2856, 1.7219,
"VM", 4, 0.7267, 0.2734, 0.3738, 1.8077,
1.4430, 1.6066, 4.7515,
"VM21", 4, 0.1429, 0.8571, 0.0001, 0.1446,
0.0001, 0.1428, 0.0218,
"VM22", 4, 0.5000, 0.5000, 0.2000, 0.2000,
0.2000, 0.2000, 0.2000, /* no data */
"VMK", 4, 0.5000, 0.5000, 0.2000, 0.2000,
0.2000, 0.2000, 0.2000, /* no data */
"VV0", 5, 0.1789, 0.8211, 13.8318, 10.9183,
8.0808, 13.5666, 3.1386,
"VVD", 7, 0.1416, 0.8584, 4.2991, 5.3507,
4.3290, 9.9607, 1.4603,
"VVG", 7, 0.1111, 0.8889, 4.4860, 4.3384,
3.6075, 8.1042, 0.9154,
"VVN", 5, 0.0812, 0.9188, 14.0187, 12.2198,
10.3896, 12.9597, 1.3078,
"VVZ", 7, 0.2051, 0.7948, 2.4299, 2.0969,
0.7215, 3.8558, 0.8718,
"XX", 6, 0.2340, 0.7660, 0.1869, 1.8077,
1.1544, 1.3567, 0.4795,
"ZZ1", 5, 0.0417, 0.9583, 0.1250, 0.4167,
0.0417, 0.3750, 0.0417,
"{CP}", 8, 0.9000, 0.1000, 0.2500, 0.2500,
0.1000, 0.3000, 0.1000,
".", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
":", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
162

"", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
"!", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
"", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
",", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
"-", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
"(", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
")", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000,
"'", 0, 1.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000}
float transUU[10][10] = {
1.0000, 0.8538, 0.9589, 0.9754, 0.7731, 0.2040,
0.1466, 0.1473, 0.0353, 0.4330,
0.4907, 0.6492, 0.8084, 0.8437, 0.4118, 0.0866,
0.1250, 0.0495, 0.0232, 0.2447,
0.8333, 0.7733, 0.9067, 0.8786, 0.6439, 0.0651,
0.1064, 0.1006, 0.0312, 0.1200,
0.9767, 0.7451, 0.9583, 0.9808, 0.7921, 0.0829,
0.0811, 0.0390, 0.0314, 0.3193,
0.7241, 0.5031, 0.6804, 0.5864, 0.5027, 0.0816,
0.1395, 0.0824, 0.0167, 0.0976,
0.0432, 0.0646, 0.0773, 0.1107, 0.0561, 0.0185,
0.0093, 0.0200, 0.0044, 0.0675,
0.0463, 0.0750, 0.1667, 0.1026, 0.3846, 0.1140,
0.0385, 0.0000, 0.0140, 0.0163,
0.0614, 0.0711, 0.1250, 0.1126, 0.1333, 0.0000,
0.0256, 0.0000, 0.0106, 0.0631,
0.0592, 0.0769, 0.1036, 0.0883, 0.0787, 0.0240,
0.0652, 0.0037, 0.0162, 0.0345,
0.0495, 0.3468, 0.1143, 0.3497, 0.2500, 0.0373,
0.1163, 0.0000, 0.0252, 0.1074}
float transUS[10][10] = {
0.0000, 0.1462, 0.0411, 0.0246, 0.2269, 0.7960,
0.8534, 0.8527, 0.9647, 0.5670,
0.0000, 0.1672, 0.0599, 0.0154, 0.4118, 0.8524,
0.7372, 0.8960, 0.8971, 0.6011,
0.0000, 0.1700, 0.0800, 0.0347, 0.2879, 0.9118,
0.8085, 0.8742, 0.9688, 0.8400,
0.0000, 0.2549, 0.0417, 0.0082, 0.1980, 0.9058,
0.9189, 0.9610, 0.9467, 0.6807,
0.0000, 0.2147, 0.0731, 0.0500, 0.2350, 0.7814,
0.4302, 0.8353, 0.7333, 0.6829,
0.0000, 0.0094, 0.0000, 0.0033, 0.0561, 0.2319,
0.2710, 0.1200, 0.1274, 0.2068,
0.0000, 0.0000, 0.0556, 0.0000, 0.1538, 0.3161,
0.2308, 0.0909, 0.1821, 0.1138,
0.0000, 0.0133, 0.0000, 0.0033, 0.0000, 0.1937,
163

0.1795, 0.1111, 0.2979, 0.1441,
0.0000, 0.0071, 0.0000, 0.0000, 0.0140, 0.1226,
0.1739, 0.0787, 0.1542, 0.1207,
0.0000, 0.0289, 0.0000, 0.0061, 0.0500, 0.4191,
0.3953, 0.0625, 0.4137, 0.6694}
float transSU[10][10] = {
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000,
0.5093, 0.1672, 0.1317, 0.1401, 0.1765, 0.0165,
0.0929, 0.0149, 0.0232, 0.1011,
0.1667, 0.0567, 0.0133, 0.0867, 0.0682, 0.0063,
0.0851, 0.0126, 0.0000, 0.0200,
0.0233, 0.0000, 0.0000, 0.0110, 0.0099, 0.0063,
0.0000, 0.0000, 0.0037, 0.0000,
0.2759, 0.2577, 0.2466, 0.3591, 0.2404, 0.0375,
0.3721, 0.0706, 0.0333, 0.1707,
0.9568, 0.8642, 0.8969, 0.8826, 0.7296, 0.1277,
0.1869, 0.1950, 0.1051, 0.4135,
0.9537, 0.8250, 0.7778, 0.8974, 0.4615, 0.1813,
0.0962, 0.0909, 0.2129, 0.4959,
0.9386, 0.8044, 0.8250, 0.8742, 0.8333, 0.1309,
0.1538, 0.1111, 0.0638, 0.3604,
0.9408, 0.8281, 0.8679, 0.9085, 0.7303, 0.2091,
0.2609, 0.1873, 0.1571, 0.4483,
0.9505, 0.5838, 0.8571, 0.6442, 0.7000, 0.1328,
0.1395, 0.3750, 0.2014, 0.0661}
float transSS[10][10] = {
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0164, 0.0000, 0.0009, 0.0000, 0.0445,
0.0449, 0.0396, 0.0565, 0.0532,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0168,
0.0000, 0.0126, 0.0000, 0.0200,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0050,
0.0000, 0.0000, 0.0181, 0.0000,
0.0000, 0.0245, 0.0000, 0.0045, 0.0219, 0.0995,
0.0581, 0.0118, 0.2167, 0.0488,
0.0000, 0.0618, 0.0258, 0.0033, 0.1582, 0.6218,
0.5327, 0.6650, 0.7631, 0.3122,
0.0000, 0.1000, 0.0000, 0.0000, 0.0000, 0.3886,
0.6346, 0.8182, 0.5910, 0.3740,
0.0000, 0.1111, 0.0500, 0.0099, 0.0333, 0.6754,
0.6410, 0.7778, 0.6277, 0.4324,
0.0000, 0.0879, 0.0286, 0.0032, 0.1770, 0.6442,
0.5000, 0.7303, 0.6725, 0.3966,
0.0000, 0.0405, 0.0286, 0.0000, 0.0000, 0.4108,
0.3488, 0.5625, 0.3597, 0.1570}
float bigram[5][5] = {
0.0000, 0.0591, 0.2698, 0.0879, 0.5832,
0.8212, 0.0393, 0.0362, 0.0096, 0.0936,
164

0.5949, 0.0158, 0.1164, 0.0543, 0.2185,
0.5241, 0.0060, 0.0442, 0.0145, 0.4112,
0.2697, 0.1007, 0.2665, 0.0725, 0.2906}
/* floats above produced from normalising across each line of data below: */
/* 0, 774, 3535, 1151, 7640,
/* 2131, 102, 94, 25, 243,
/* 5309, 141, 1039, 485, 1950,
/* 1481, 17, 125, 41, 1162,
/* 4180, 1561, 4130, 1124, 4505
/* T R F V S */
float bigram5[6][6] = {
0.0000, 0.0240, 0.0893, 0.0283, 0.2225, 0.6358,
0.7877, 0.0131, 0.0154, 0.0077, 0.0493, 0.1268,
0.5417, 0.0049, 0.0341, 0.0242, 0.1201, 0.2750,
0.4112, 0.0035, 0.0262, 0.0088, 0.2481, 0.3022,
0.2430, 0.0615, 0.1536, 0.0430, 0.1572, 0.3417,
0.0567, 0.0478, 0.1913, 0.0590, 0.3179, 0.3273}
/* floats above produced from normalising across each line of data below: */
/* 0, 319, 1185, 375, 2953, 8437,
/* 2044, 34, 40, 20, 128, 329,
/* 4834, 44, 304, 216, 1072, 2454,
/* 1162, 10, 74, 25, 701, 854,
/* 3767, 953, 2381, 666, 2436, 5297,
/* 1463, 1235, 4939, 1524, 8210, 8453
/* TUB RISE FALL R-F STR USTR */
float trigram[3][3][3] ={
0.0261, 0.0808, 0.0050,
0.0722, 0.0748, 0.0818,
0.0112, 0.0080, 0.0019,
0.0429, 0.0711, 0.0145,
0.0262, 0.0304, 0.0743,
0.0981, 0.0558, 0.0160,
0.0429, 0.0768, 0.0016,
0.0301, 0.0258, 0.0138,
0.0120, 0.0058, 0.0000}
main()
{
int i, j, k, l, pos, done, finished, w, numberstates, s,
tub = 0, p1, p2, tri1, tri2, tri3
double value, val1, val2, bigvalue, tp
short int state[MAXWORDS], beststates[NUMBEST][MAXWORDS]
short int grp[MAXWORDS], bigstate[MAXWORDS]
float sentence2[MAXWORDS][2], sentence6[MAXWORDS][6]
float beststatevals[NUMBEST]
char wordtag[MAXWORDS][7], prosody[MAXWORDS][24]
for (i = 0 i < NUMBEST i++)
165

eststatevals[i] =0 /* clear best states */
scanf("%d", &w) /* get number of words */
if (w < 0 || w > MAXWORDS) {
printf("Number of words(%d) not suitable. Exiting\n",
w)
exit(0)
}
for (i = 0 i < w i++) {
scanf("%s %s", wordtag[i], prosody[i])
for (j = 0 j < NUMTAGS j++)
if (!strcmp(tags[j].tagname, wordtag[i])) {
sentence2[i][0] = tags[j].prb2U
sentence2[i][1] = tags[j].prb2S
grp[i] = tags[j].group
break
}
}
if (j == NUMTAGS) {/* unknown tag assume multiple punc */
sentence2[i][0] = 1.0
sentence2[i][1] = 0.0
grp[i] = 0
}
/* number of possible states 2^w */
numberstates = pow(2.0, (double)w)
for (j = 0 j < w j++)
state[j] = 0 /* set up first state */
for (i = 0 i < numberstates i++) {
val1 = 1.0
/* product of prob tag being in its state */
for (j = 0 j < w j++)
val1 *= sentence2[j][state[j]]
val2 = 1.0 /* group transition probabilities */
for (j = 1 j < w j++) {
if (state[j-1] == 0 && state[j] == 0)
tp = transUU[grp[j-1]][grp[j]]
if (state[j-1] == 0 && state[j] == 1)
tp = transUS[grp[j-1]][grp[j]]
if (state[j-1] == 1 && state[j] == 0)
tp = transSU[grp[j-1]][grp[j]]
if (state[j-1] == 1 && state[j] == 1)
tp = transSS[grp[j-1]][grp[j]]
val2 *= tp
}
if (w > 3)
for (j = 2 j < w j++) { /* stress trigram probs */
if (grp[j-2] == 0)
tri1 = 2
else
tri1 = state[j-2]
if (grp[j-1] == 0)
166

}
tri2 = 2
else
tri2 = state[j-1]
if (grp[j-0] == 0)
tri3 = 2
else
tri3 = state[j]
val2 *= trigram[tri1][tri2][tri3]
value = val1 * val2
/* keep track of the top NUMBEST most probable sequences */
for (j = 0 value < beststatevals[j] && j < NUMBEST
j++)
if (j < NUMBEST) {
for (k = NUMBEST - 1 k > j k--) {/* make room */
beststatevals[k] = beststatevals[k-1]
for (l = 0 l < w l++)
beststates[k][l] = beststates[k-1][l]
}
beststatevals[j] = value
for (k = 0 k < w k++)
beststates[j][k] = state[k]
}
}
pos = 0 /* update state[] to next state */
do {
if (++state[pos] == 2) {
state[pos] = 0
pos++
done = 0
} else
done = 1
} while (pos < w && !done)
/* NOW to use the NUMBEST sequences held in */
/* beststates[NUMBEST][MAXWORDS] to predict the TSMs */
bigvalue = 0.0 /* reset best value */
/* do this for each state from above */
for (l = NUMBEST - 1 l >= 0 l--) {
for (j = 0 j < w j++) /* setup state */
if (beststates[l][j] == 0)
state[j] = 5 /* 0=unstr -> 5*/
else
state[j] = 1 /* 1=stressed -> 1 */
for (i = 0 i < w i++) { /* setup probability lattice */
for (j = 0 j < NUMTAGS j++)
if (!strcmp(tags[j].tagname, wordtag[i])) {
sentence6[i][0] = 0.0 /* TUB */
sentence6[i][1] = tags[j].prb5R
sentence6[i][2] = tags[j].prb5F
167

}
sentence6[i][3] = tags[j].prb5V
sentence6[i][4] = tags[j].prb5S
sentence6[i][5] = tags[j].prb5U
if (sentence6[i][1] + sentence6[i][2]
+ sentence6[i][3] + sentence6[i][4]
+ sentence6[i][5] == 0 ) {
sentence6[i][0]
= 1.0 /* if all probs = 0 */
state[i] = 0 /* then must be punc */
}
break
}
if (j == NUMTAGS) {
sentence6[i][0] = 1.0 /* TUB */
sentence6[i][1] = 0.0
sentence6[i][2] = 0.0
sentence6[i][3] = 0.0
sentence6[i][4] = 0.0
sentence6[i][5] = 0.0
state[i] = 0
}
finished = 0
while (!finished) {
value = 1.0
val1 = 1.0
val2 = 1.0
for (j = 0 j < w j++) /* initial state probs */
val1 *= (double)sentence6[j][state[j]]
for (j = 1 j < w j++) /* all state trans probs */
val2 *= (double)bigram5[state[j-1]][state[j]]
/* non stress state trans probs */
for (p1 = 0, j = 0 j < w j++)
if (state[j] != 5 && state[j] !=
0)
p1++ /* count no. of stresses */
if (p1 >= 2) {
/* mult state-to-state trans probs ignoring unstr */
for (p1 = 0 state[p1] != 5 && state[p1]
!= 0 p1++)
/* find first stress*/
for (p2 = (p1 + 1) state[p2] !=
5 && state[p2] != 0 && p2 < w p2++) {
if (p2 < w)
val2 *= (double)bigram[state[p1]][state[p2]]
p1 = p2
}
}
/* initial state probs * transition state probs */
value = val1 * val2
if (value > bigvalue) {
/*printf("BEST SO FAR:")*/
bigvalue = value
for (k = 0 k < w k++) {
bigstate[k] = state[k] /* save new best state */
168

}
/*switch(state[k]) {
case 0:
printf("|")
break
case 1:
printf("R")
break
case 2:
printf("F")
break
case 3:
printf("V")
break
case 4:
printf("S")
break
case 5:
printf("U")
}*/
}
/*printf(" %e\n",value)*/
}
pos = w - 1 /* update state[] to next state */
do {
done = 1 /* by default have done unless changed below */
if (state[pos] == 0 || state[pos]
== 5) {/* don't change tub or unstressed state */
pos--
done = 0
} else if (pos >= 0) {/* don't go past end */
if (++state[pos] == 5) {/* if state incs to 5=unstressed */
state[pos] =1 /* reset to 1 */
pos-- /* and point to next word */
done = 0 /* and say we haven't done */
}
}
if (pos < 0)
finished = 1 /* ie have done all combinations */
} while (pos >= 0 && !done)
}
for (i = 0 i < w i++)
printf("%s=%s ", wordtag[i], prosody[i])
printf("\nPredicted: ")
for (i = 0 i < w i++) {
switch (bigstate[i]) {
case 0:
printf("|")
break
case 1:
printf("R")
break
case 2:
printf("F")
break
case 3:
169

}
printf("V")
break
case 4:
printf("S")
break
case 5:
printf("U")
}
}
printf("\nShould Be: ")
for (i = 0 i < w i++) {
if (bigstate[i] == 0)
s = 0 /* tu boundary */
else
for (s = 5, j = 0 j < strlen(prosody[i])
j++) {
if ((prosody[i][j] == ',' || prosody[i][j]
== '/') && s >= 4)
s = 1
if ((prosody[i][j] == '\\' || prosody[i][j]
== '`') && s >= 4)
s = 2
if ((prosody[i][j] == ',' || prosody[i][j]
== '/') && s == 2)
s = 3
if ((prosody[i][j] == '*' || prosody[i][j]
== '_' || prosody[i][j] == '~') &&
s == 5)
s = 4
if ((prosody[i][j] == '') && s == 5)
s = 4
}
switch (s) {
case 0:
printf("|")
break
case 1:
printf("R")
break
case 2:
printf("F")
break
case 3:
printf("V")
break
case 4:
printf("S")
break
case 5:
printf("U")
}
}
printf("\n")
170

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