Exploiting models intrinsic robustness for noisy speech recognition

Christophe Cerisara 1 Dominique Fohr 1 Odile Mella 1 Irina Illina 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We propose in this paper an original approach to build masks in the framework of missing data recognition. The proposed soft masks are estimated from the models themselves, and not from the test signal as it is usually the case. They represent the intrinsic robustness of model's log-spectral coefficients. The method is validated with cepstral models, on two synthetic and two real-life noises, at different signal-to-noise ratios. We further discuss how such masks can be combined with other signal-based masks and noise compensation techniques.
Mots-clés : reconnaissance de la parole missing data speech recognition robustness robustesse données manquantes
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Communication dans un congrès
8th International Conference on Spoken Language Processing - ICSLP'2004, 2004, Jeju, Corée du Sud, 4 p, 2004
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Soumis le : mardi 26 septembre 2006 - 10:03:33
Dernière modification le : vendredi 9 février 2018 - 13:20:05
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Christophe Cerisara, Dominique Fohr, Odile Mella, Irina Illina. Exploiting models intrinsic robustness for noisy speech recognition. 8th International Conference on Spoken Language Processing - ICSLP'2004, 2004, Jeju, Corée du Sud, 4 p, 2004. 〈inria-00099890〉

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