Oscillations, Waves, and Interactions - GWDG

Speech research 35
[13] D. Michaelis, Das Göttinger Heiserkeits-Diagramm – Entwicklung und Prüfung
eines akustischen Verfahrens zur objektiven Stimmgütebeurteilung pathologischer
Stimmen, Doctoral thesis, Universität Göttingen, downloadable at
http://webdoc.sub.gwdg.de/diss/2000/michaelis/ (2000).
[14] M. Fröhlich, D. Michaelis, H. W. Strube, and E. Kruse, ‘Acoustic Voice Analysis by
Means of the Hoarseness Diagram’, J. Speech, Language, and Hearing Res. 43, 706
(2000).
[15] H. Kasuya, Y. Endo, and S. Saliu, ‘Novel acoustic measurements of jitter and shimmer
characteristics from pathological voice’, in Eurospeech ’93 Proceedings, edited by
K. Fellbaum (Berlin, 1993), vol. 3, pp. 1973–1976.
[16] Y. Medan, E. Yair, and D. Chazan, ‘Super resolution pitch determination of speech
signals’, IEEE Trans. Signal Processing 39, 40 (1991).
[17] I. R. Titze and H. Liang, ‘Comparison of F0 extraction methods for high precision voice
perturbation measurement’, J. Speech Hear. Res. 36, 1120 (1993).
[18] H. Kasuya, S. Ogawa, Y. Kikuchi, and S. Ebihara, ‘Normalized noise energy as an
acoustic measure to evaluate pathologic voice’, J. Acoust. Soc. Am. 80, 1329 (1986).
[19] G. de Krom, ‘A cepstrum-based technique for determining a harmonics-to-noise ratio
in speech signals’, J. Speech Hear. Res. 36, 224 (1993).
[20] D. Michaelis, T. Gramss, and H. W. Strube, ‘Glottal-to-Noise Excitation Ratio – a New
Measure for Describing Pathological Voices’, Acustica / acta acustica 83, 700 (1997).
[21] T. Kohonen, Self-Organizing Maps, vol. 30 of Springer Series in Information Sciences
(Springer, Berlin, 2001), 3rd ed.
[22] J. Kiosses and H. W. Strube, ‘Akustische Charakteristiken der Normalstimme’, in
Fortschritte der Akustik – DAGA 2001, edited by O. von Estorff (DEGA, Oldenburg,
2001), pp. 84–85.
[23] M. Fröhlich, D. Michaelis, J. Lessing, and H. W. Strube, “‘Breathiness measures” in
acoustic voice analysis’, in Advances in Quantitative Laryngoscopy, Voice and Speech
Research, Proc. 4th International Workshop, edited by T. Braunschweig, J. Hanson,
P. Schelhorn-Neise, and H. Witte (Friedrich-Schiller-Universität, Jena, 2000), pp. 63–
71.
[24] A. van Ooyen and B. Nienhuis, ‘Improving the Convergence of the Backpropagation
Algorithm’, Neural Networks 5, 465 (1992).
[25] J. Lessing, Entwicklung einer Klassifikationsmethode zur akustischen Analyse fortlaufender
Sprache unterschiedlicher Stimmgüte mittels Neuronaler Netze und deren
Anwendung, Doctoral thesis, Universität Göttingen (2007).
[26] J. Lessing, M. Fröhlich, D. Michaelis, H. W. Strube, and E. Kruse, ‘Verwendung
neuronaler Netze zur Stimmgütebeschreibung pathologischer Stimmen’, in Aktuelle
phoniatrisch-pädaudiologische Aspekte 1998, edited by M. Gross (Median, Heidelberg,
1999), vol. 6, pp. 39–43.
[27] J. Lessing, M. Fröhlich, D. Michaelis, H. W. Strube, and E. Kruse, ‘Akustische Stimmanalyse
aus fortlaufender Sprache – Untersuchung von Tumorgruppen’, in Aktuelle
phoniatrisch-pädaudiologische Aspekte 1998, edited by M. Gross (Median, Heidelberg,
1999), vol. 6, pp. 126–130.
[28] J. Lessing, M. Fröhlich, D. Michaelis, H. W. Strube, and E. Kruse, ‘A Neural Network
Based Method to Assess Voice Quality from Continuous Speech for Different Voice Disorders’,
in Advances in Quantitative Laryngoscopy, Voice and Speech Research, Proc.
4th International Workshop, edited by T. Braunschweig, J. Hanson, P. Schelhorn-Neise,
and H. Witte (Friedrich-Schiller-Universität, Jena, 2000), pp. 124–131.
[29] G. Fant, J. Liljencrants, and Q. Lin, A four-parameter model of glottal flow, Speech
Transmission Laboratory – Quarterly Progress and Status Report 4/1985, Stockholm