A discriminative segmental speech model and its application to hungarian number recognition

László Tóth, András Kocsor, Kornél Kovács

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

8 Citations (Scopus)

Abstract

This paper presents a stochastic segmental speech recogniser that models the a posteriori probabilities directly. The main issues concerning the system are segmental phoneme classification, utterance-level aggregation and the pruning of the search space. For phoneme classification, artificial neural networks and support vector machines are applied. Phonemic segmentation and utterancelevel aggregation is performed with the aid of anti-phoneme modelling. At the phoneme level, the system convincingly outperforms theHMMsystem trained on the same corpus, while at the word level it attains the performance of the HMM system trained without embedded training.

Original languageEnglish
Title of host publicationText, Speech and Dialogue - 3rd International Workshop, TSD 2000, Proceedings
EditorsIvan Kopecek, Karel Pala, Petr Sojka
PublisherSpringer Verlag
Pages307-313
Number of pages7
ISBN (Print)3540410422, 9783540410423
Publication statusPublished - Jan 1 2000
Event3rd International Workshop on Text, Speech and Dialogue, TSD 2000 - Brno, Czech Republic
Duration: Sep 13 2000Sep 16 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1902
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Text, Speech and Dialogue, TSD 2000
CountryCzech Republic
CityBrno
Period9/13/009/16/00

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tóth, L., Kocsor, A., & Kovács, K. (2000). A discriminative segmental speech model and its application to hungarian number recognition. In I. Kopecek, K. Pala, & P. Sojka (Eds.), Text, Speech and Dialogue - 3rd International Workshop, TSD 2000, Proceedings (pp. 307-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1902). Springer Verlag.