Towards Missing Data Recognition with Cepstral Features

Christophe Cerisara 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We study in this work the Missing Data Recognition (MDR) framework applied to a large vocabulary continuous speech recognition (LVCSR) task with cepstral models when the speech signal is corrupted by musical noise. We do not propose a full system that solves this difficult problem, but we rather present some of the issues involved and study some possible solutions to them. We focus in this work on the issues concerning the application of masks to cepstral models. We further identify possible errors and study how some of them affect the performances of the system.
Type de document :
Communication dans un congrès
8th European Conference on Speech Communication and Technology - EUROSPEECH'03, Sep 2003, Geneva, Switzerland, 4 p, 2003
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Soumis le : jeudi 19 octobre 2006 - 09:03:59
Dernière modification le : vendredi 9 février 2018 - 13:20:05
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  • HAL Id : inria-00107648, version 1

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Christophe Cerisara. Towards Missing Data Recognition with Cepstral Features. 8th European Conference on Speech Communication and Technology - EUROSPEECH'03, Sep 2003, Geneva, Switzerland, 4 p, 2003. 〈inria-00107648〉

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