Presentation 2001/12/13
Noise Robust Speech Recognition by Integration of MLLR Adaptation and Feature Extraction for Noise Reduced Speech
Masakiyo Fujimoto, Yasuo Ariki,
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Abstract(in English) In this paper, we investigate a noise robust acoustic feature in our proposed noise robust speech recognition method using Kalman filtering for speech signal estimation and iterative unsupervised MLLR adaptation. For the noise robust acoustic feature, we employed root cepstral coefficients and compared the results with conventionally used MFCCs at speech recognition accuracy. Furthermore, we investigate the number of phoneme clusters in MLLR adaptation. In order to evaluate the proposed method, we carried out large vocabulary continuous speech recognition experiments under 3 types of music. As a result, the proposed method showed the significant improvement in word accuracy.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) noise robust speech recognition / non-stationary noise / Kalman filter / unsupervised MLLR adaptation / root cepstral coefficient
Paper # NLC2001-58,SP2001-93
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Committee NLC
Conference Date 2001/12/13(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 by Integration of MLLR Adaptation and Feature Extraction for Noise Reduced Speech
Sub Title (in English)
Keyword(1) noise robust speech recognition
Keyword(2) non-stationary noise
Keyword(3) Kalman filter
Keyword(4) unsupervised MLLR adaptation
Keyword(5) root cepstral coefficient
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 2001/12/13
Paper # NLC2001-58,SP2001-93
Volume (vol) vol.101
Number (no) 520
Page pp.pp.-
#Pages 6
Date of Issue