Development and comparison of three syllable stress classifiers

Karen L. Jenkin, Michael S Scordilis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

This paper describes the development of three alternative techniques for the classification of syllable stress in fluent speech. They are based on: (1) neural networks that use contextual syllabic information, (2) first and second order Markov chains that depend on a new dynamic vector quantization approach, and (3) a rule-based approach. Both the neural network and the statistical approach achieved performance above 80%, with the neural networks slightly outperforming the Markov models. Experimental results also show that stress classification could enhance speech recognition.

Original languageEnglish
Title of host publicationInternational Conference on Spoken Language Processing, ICSLP, Proceedings
Editors Anon
Pages733-736
Number of pages4
Volume2
StatePublished - Dec 1 1996
Externally publishedYes
EventProceedings of the 1996 International Conference on Spoken Language Processing, ICSLP. Part 1 (of 4) - Philadelphia, PA, USA
Duration: Oct 3 1996Oct 6 1996

Other

OtherProceedings of the 1996 International Conference on Spoken Language Processing, ICSLP. Part 1 (of 4)
CityPhiladelphia, PA, USA
Period10/3/9610/6/96

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Jenkin, K. L., & Scordilis, M. S. (1996). Development and comparison of three syllable stress classifiers. In Anon (Ed.), International Conference on Spoken Language Processing, ICSLP, Proceedings (Vol. 2, pp. 733-736)