InforLorV4, Main, Exploration, bibRecord, 004D47

On noise masking for automatic missing data speech recognition : A survey and discussion

Identifieur interne : 004D47 ( Main/Exploration ); précédent : 004D46; suivant : 004D48

On noise masking for automatic missing data speech recognition : A survey and discussion

Auteurs : Christophe Cerisara [France] ; Sébastien Demange [France] ; Jean-Paul Haton [France]

Source :

Computer speech & language : (Print) [ 0885-2308 ] ; 2007.

RBID : Francis:09-0009259

Descripteurs français

Pascal (Inist)
- Linguistique informatique.

English descriptors

KwdEn :
- Computational linguistics.

Abstract

Automatic speech recognition (ASR) has reached very high levels of performance in controlled situations. However, the performance degrades significantly when environmental noise occurs during the recognition process. Nowadays, the major challenge is to reach a good robustness to adverse conditions, so that automatic speech recognizers can be used in real situations. Missing data theory is a very attractive and promising approach. Unlike other denoising methods, missing data recognition does not match the whole data with the acoustic models, but instead considers part of the signal as missing, i.e. corrupted by noise. While speech recognition with missing data can be handled efficiently by methods such as data imputation or marginalization, accurately identifying missing parts (also called masks) remains a very challenging task. This paper reviews the main approaches that have been proposed to address this problem. The objective of this study is to identify the mask estimation methods that have been proposed so far, and to open this domain up to other related research, which could be adapted to overcome this difficult challenge. In order to restrict the range of methods, only the techniques using a single microphone are considered.

Url:

https://hal.inria.fr/inria-00160554

Affiliations:

Le document en format XML

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<front><div type="abstract" xml:lang="en">Automatic speech recognition (ASR) has reached very high levels of performance in controlled situations. However, the performance degrades significantly when environmental noise occurs during the recognition process. Nowadays, the major challenge is to reach a good robustness to adverse conditions, so that automatic speech recognizers can be used in real situations. Missing data theory is a very attractive and promising approach. Unlike other denoising methods, missing data recognition does not match the whole data with the acoustic models, but instead considers part of the signal as missing, i.e. corrupted by noise. While speech recognition with missing data can be handled efficiently by methods such as data imputation or marginalization, accurately identifying missing parts (also called masks) remains a very challenging task. This paper reviews the main approaches that have been proposed to address this problem. The objective of this study is to identify the mask estimation methods that have been proposed so far, and to open this domain up to other related research, which could be adapted to overcome this difficult challenge. In order to restrict the range of methods, only the techniques using a single microphone are considered.</div>
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On noise masking for automatic missing data speech recognition : A survey and discussion

On noise masking for automatic missing data speech recognition : A survey and discussion

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

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