Development and comparison of three syllable stress classifiers

Author

Jenkin, Karen L. ; Scordilis, Michael S.

Author_Institution

Telstra Res. Labs., Clayton, Vic., Australia

Volume

2

fYear

1996

fDate

3-6 Oct 1996

Firstpage

733

Abstract

The 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

Keywords

Markov processes; knowledge based systems; neural nets; pattern classification; speech processing; speech recognition; vector quantisation; Markov models; contextual syllabic information; dynamic vector quantization approach; fluent speech; neural networks; rule based approach; second order Markov chains; speech recognition; statistical approach; stress classification; syllable stress classifiers; Australia; Data mining; Laboratories; Neural networks; Signal processing; Speech processing; Speech recognition; Stress; Vector quantization; Wire;

fLanguage

English

Publisher

ieee

Conference_Titel

Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on

Conference_Location

Philadelphia, PA

Print_ISBN

0-7803-3555-4

Type

conf

DOI

10.1109/ICSLP.1996.607466

Filename

607466