On noise masking for automatic missing data speech recognition : A survey and discussion
Identifieur interne : 000298 ( PascalFrancis/Corpus ); précédent : 000297; suivant : 000299On noise masking for automatic missing data speech recognition : A survey and discussion
Auteurs : Christophe Cerisara ; Sébastien Demange ; Jean-Paul HatonSource :
- Computer speech & language : (Print) [ 0885-2308 ] ; 2007.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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.
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Format Inist (serveur)
| NO : | FRANCIS 09-0009259 INIST |
|---|---|
| ET : | On noise masking for automatic missing data speech recognition : A survey and discussion |
| AU : | CERISARA (Christophe); DEMANGE (Sébastien); HATON (Jean-Paul) |
| AF : | LORIA, UMR 7503/Nancy/France (1 aut., 2 aut., 3 aut.) |
| DT : | Publication en série; Niveau analytique |
| SO : | Computer speech & language : (Print); ISSN 0885-2308; Royaume-Uni; Da. 2007; Vol. 21; No. 3; Pp. 443-457; Bibl. 1 p.3/4 |
| LA : | Anglais |
| EA : | 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. |
| CC : | 52478; 524 |
| FD : | Linguistique informatique |
| ED : | Computational linguistics |
| LO : | INIST-21332.354000145646040020 |
| ID : | 09-0009259 |
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Francis:09-0009259Le document en format XML
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