Recognition and Rejection Performance in Wordspotting Systems Using Hidden Markov modeling techniques

Yassine Benayed 1 Dominique Fohr 1 Jean-Paul Haton 1 Gérard Chollet 2
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 TSI - Laboratoire du traitement du signal et instrumentation
Abstract : This paper deals with the problem of acceptance/rejection of recognition hypotheses for continuous speech utterances. Two different techniques are investigated to improve the rejection of out-of-vocabulary (OOV) words. A combined approach is first proposed which uses two garbage models (a trained one and an on-line garbage model). The second method uses the trained garbage model and consists in post-processing the recognizer hypotheses by computing for each of them a confidence measure. Both approaches are evaluated in the context of a stock exchange application through the telephone for French. The parameters of the two approaches are studied to improve recognition accuracy.
Mots-clés : spotting modèles de markov cachés keywords hidden markov models
Type de document :
Communication dans un congrès
International Workshop speech and computer - SPECOM'2002, Sep 2002, St-Petersburg, Russia, 4 p, 2002
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https://hal.inria.fr/inria-00100841
Contributeur : Publications Loria <>
Soumis le : mardi 26 septembre 2006 - 14:52:20
Dernière modification le : jeudi 11 janvier 2018 - 06:19:55

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  • HAL Id : inria-00100841, version 1

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INRIA | UNIV-ST-ETIENNE | LAHC | LORIA | UNIV-LORRAINE

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Yassine Benayed, Dominique Fohr, Jean-Paul Haton, Gérard Chollet. Recognition and Rejection Performance in Wordspotting Systems Using Hidden Markov modeling techniques. International Workshop speech and computer - SPECOM'2002, Sep 2002, St-Petersburg, Russia, 4 p, 2002. 〈inria-00100841〉

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