Presentation 2002/12/12
Noise Robust Speech Recognition Using GMM Based Speech Estimation Method
Masakiyo FUJIMOTO, Yasuo ARIKI,
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Abstract(in English) In this paper, a noise robust speech recognition method is proposed, by combining temporal domain singular value decomposition(SVD) based speech enhancement and Gaussian mixture model(GMM) based speech estimation. The critical neck of the GMM based approach is the noise estimation problem. For this noise estimation problem, we investigated the adaptive noise estimation in the GMM based approach. Furthermore, in order to obtain higher recognition accuracy, we employed a temporal domain SVD based speech enhancement method as the pre-processing module of the GMM based approach. In addition, to reduce more influence of the noise included in the noisy speech, we introduce an adaptive over subtraction factor into the temporal domain SVD based speech enhancement. In evaluation on the AURORA2 tasks, our method showed the significant improvement in the recognition accuracy at all the noise conditions.
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Keyword(in English) noise robust speech recognition / GMM based speech estimation / temporal domain SVD / AURORA2 database
Paper # NLC2002-48
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Committee NLC
Conference Date 2002/12/12(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Noise Robust Speech Recognition Using GMM Based Speech Estimation Method
Sub Title (in English)
Keyword(1) noise robust speech recognition
Keyword(2) GMM based speech estimation
Keyword(3) temporal domain SVD
Keyword(4) AURORA2 database
1st Author's Name Masakiyo FUJIMOTO
1st Author's Affiliation Faculty of Science and Technology, Ryukoku University()
2nd Author's Name Yasuo ARIKI
2nd Author's Affiliation Faculty of Science and Technology, Ryukoku University
Date 2002/12/12
Paper # NLC2002-48
Volume (vol) vol.102
Number (no) 527
Page pp.pp.-
#Pages 6
Date of Issue