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

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.
Type de document :
Communication dans un congrès
International Workshop speech and computer - SPECOM'2002, Sep 2002, St-Petersburg, Russia, 4 p, 2002
Liste complète des métadonnées

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

Identifiants

  • HAL Id : inria-00100841, version 1

Citation

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〉

Partager

Métriques

Consultations de la notice

193