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Feature-dependent compensation of coders in speech recognition

Identificadores del recurso

http://hdl.handle.net/10533/197801

doi: 10.1016/j.sigpro.2005.03.019

wos: WOS:000233950200004

eissn: 0

issn: 0165-1684

Procedencia

(LA Referencia)

Ficha

Título:: Feature-dependent compensation of coders in speech recognition
Descripción:: A solution to the problem of speech recognition with signals corrupted by coders is presented. The coding-decoding distortion is modelled as feature dependent. This model is employed to propose an unsupervised expectation-maximization (EM) estimation algorithm of the coding-decoding distortion that is able to cancel the effect of coders with as few as one adapting utterance. No knowledge about the coder is required. The feature-dependent adaptation can give a word error rate (WER) 21% lower than the feature-independent model. Finally, when compared to the baseline system, the reduction in WER can be as high as 70%. (c) 2005 Elsevier B.V. All rights reserved.
Fuente:: SIGNAL PROCESSING (PRINT); reponame:Artículos CONICYT; instname:CONICYT Chile; instacron:CONICYT
Idioma:: English
Relación:: instname: Conicyt; reponame: Repositorio Digital RI2.0; info:eu-repo/grantAgreement/Fondef/D02I1089; info:eu-repo/semantics/dataset/hdl.handle.net/10533/93477
Ámbito geográfico o temporal:: NLD; AMSTERDAM
Autor/Productor:: Becerra-Yoma, Nestor; Molina-Sánchez, Carlos
Editor:: ELSEVIER SCIENCE BV
Derechos:: info:eu-repo/semantics/openAccess
Fecha:: 2006
Tipo de recurso:: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion
About:: 2020-01-27T14:04:19Zhttp://www.openarchives.org/OAI/2.0/oai_dc/Artículos CONICYT - CONICYT Chile

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