4th International Conference on Spoken Language Processing

Philadelphia, PA, USA
October 3-6, 1996

Development and Comparison of Three Syllable Stress Classifiers

Karen L. Jenkin (1), Michael S. Scordilis (2)

(1) Telstra Research Laboratories, Clayton, VIC, Australia
(2) Wire Communications Laboratory, University of Patras, Greece

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.

Full Paper

Bibliographic reference.  Jenkin, Karen L. / Scordilis, Michael S. (1996): "Development and comparison of three syllable stress classifiers", In ICSLP-1996, 733-736.