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Conference Paper: An Environment Compensated Maximum Likelihood Training Approach Based on Stochastic Vector Mapping

TitleAn Environment Compensated Maximum Likelihood Training Approach Based on Stochastic Vector Mapping
Authors
KeywordsEngineering
Electrical engineering
Issue Date2005
PublisherIEEE.
Citation
IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Philadelphia, Pennsylvania, USA, 18-23 March 2005, v. 1, p. 429-432 How to Cite?
AbstractSeveral recent approaches for robust speech recognition are developed based on the concept of stochastic vector mapping (SVM) that perform a frame-dependent bias removal to compensate for environmental variabilities in both training and recognition stages. Some of them require the stereo recordings of both clean and noisy speech for the estimation of SVM function parameters. In this paper, we present a detailed formulation of an maximum likelihood training approach for the joint design of SVM function parameters and HMM parameters of a speech recognizer that does not rely on the availability of stereo training data. Its learning behavior and effectiveness is demonstrated by using the experimental results on Aurora3 Finnish connected digits database recorded by using both close-talking and hands-free microphones in cars.
Persistent Identifierhttp://hdl.handle.net/10722/45527
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWu, Jen_HK
dc.contributor.authorHuo, Qen_HK
dc.contributor.authorZhu, Den_HK
dc.date.accessioned2007-10-30T06:28:29Z-
dc.date.available2007-10-30T06:28:29Z-
dc.date.issued2005en_HK
dc.identifier.citationIEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, Philadelphia, Pennsylvania, USA, 18-23 March 2005, v. 1, p. 429-432en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45527-
dc.description.abstractSeveral recent approaches for robust speech recognition are developed based on the concept of stochastic vector mapping (SVM) that perform a frame-dependent bias removal to compensate for environmental variabilities in both training and recognition stages. Some of them require the stereo recordings of both clean and noisy speech for the estimation of SVM function parameters. In this paper, we present a detailed formulation of an maximum likelihood training approach for the joint design of SVM function parameters and HMM parameters of a speech recognizer that does not rely on the availability of stereo training data. Its learning behavior and effectiveness is demonstrated by using the experimental results on Aurora3 Finnish connected digits database recorded by using both close-talking and hands-free microphones in cars.en_HK
dc.format.extent294706 bytes-
dc.format.extent2385 bytes-
dc.format.extent7254 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectEngineeringen_HK
dc.subjectElectrical engineeringen_HK
dc.titleAn Environment Compensated Maximum Likelihood Training Approach Based on Stochastic Vector Mappingen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1520-6149&volume=1&spage=429&epage=432&date=2005&atitle=An+Environment+Compensated+Maximum+Likelihood+Training+Approach+Based+on+Stochastic+Vector+Mappingen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICASSP.2005.1415142en_HK
dc.identifier.scopuseid_2-s2.0-20444395560-
dc.identifier.hkuros101981-

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