Presentation 2001/12/13
Rapid Model Adaptation with a Prior Noise GMM and Multi-SNR Models for Noisy Speech Recognition
Masaki IDA, Satoshi NAKAMURA,
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Abstract(in English) When a speech recognition system is used in a real environment, the recognition performance is affected by surrounding noise. Most additional noises are difficult to predict about kind of noise and SNR, so we cannot avoid the mismatch situation between those of training data and test data. Then we need a method to deal with mismatched noise problems and unknown SNRs. In this paper, we propose an HMM composition-based model adaptation that uses a prior noise data against noise mismatches. We also prepare plural HMMs for several SNRs and select the best model based on acoustic likelihood to deal with the unknown SNRs. Experimental results with AURORA2 task show 53% word accuracy improvement from baseline system with 1 sec real noise data for adaptation. The performance is equivalent to a case with 10 sec real data using the conventional HMM composition method.
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Keyword(in English) HMM composition / noise model / nonstationary noise / multipath model
Paper # NLC2001-57,SP2001-92
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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) Rapid Model Adaptation with a Prior Noise GMM and Multi-SNR Models for Noisy Speech Recognition
Sub Title (in English)
Keyword(1) HMM composition
Keyword(2) noise model
Keyword(3) nonstationary noise
Keyword(4) multipath model
1st Author's Name Masaki IDA
1st Author's Affiliation ATR Spoken Language Translation Research Laboratories()
2nd Author's Name Satoshi NAKAMURA
2nd Author's Affiliation ATR Spoken Language Translation Research Laboratories
Date 2001/12/13
Paper # NLC2001-57,SP2001-92
Volume (vol) vol.101
Number (no) 520
Page pp.pp.-
#Pages 6
Date of Issue