Robust speech recognition to non-stationary and unpredictable noise based on model-driven approaches

Christophe Cerisara 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 : Automatic speech recognition works quite well in clean conditions, and several algorithms have already been proposed to deal with stationary noise. The next challenge probably consists to compensate for non-stationary noise as well. This work studies this problem by proposing and comparing two adaptations of the Parallel Model Combination (PMC) algorithm for non-stationary noise. A third method, derived from the missing data framework, is further proposed and compared to the two previous ones. In musical noise, experimental results show an important improvement of the recognition accuracy for one PMC-derived algorithm, compared to the non adapted system. The missing-data algorithm also performs quite well, despite its simplicity and the strong assumptions he is using.
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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 - 09:03:58
Dernière modification le : vendredi 9 février 2018 - 13:20:05
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  • HAL Id : inria-00107647, version 1

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Christophe Cerisara, Irina Illina. Robust speech recognition to non-stationary and unpredictable noise based on model-driven approaches. 8th European Conference on Speech Communication and Technology - EUROSPEECH'03, Sep 2003, Geneva, Switzerland, 4 p, 2003. 〈inria-00107647〉

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