Maximum likelihood sub-band adaptation for robust speech recognition

Noise-robust speech recognition has become an important area of research in recent years. In current speech recognition systems, the Mel-frequency cepstrum coefficients (MFCCs) are used as recognition features. When the speech signal is corrupted by narrow-band noise, the entire MFCC feature vector...

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Bibliographic Details
Published in:Speech communication 2005-11, Vol.47 (3), p.243-264
Main Authors: Zhu, Donglai, Nakamura, Satoshi, Paliwal, Kuldip K., Wang, Renhua
Format: Article
Language:English
Subjects:
Adaptation
Applied sciences
Exact sciences and technology
Information, signal and communications theory
Pattern recognition
Robust speech recognition
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Speech processing
Sub-band
Telecommunications and information theory
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Summary:Noise-robust speech recognition has become an important area of research in recent years. In current speech recognition systems, the Mel-frequency cepstrum coefficients (MFCCs) are used as recognition features. When the speech signal is corrupted by narrow-band noise, the entire MFCC feature vector gets corrupted and it is not possible to exploit the frequency-selective property of the noise signal to make the recognition system robust. Recently, a number of sub-band speech recognition approaches have been proposed in the literature, where the full-band power spectrum is divided into several sub-bands and then the sub-bands are combined depending on their reliability. In conventional sub-band approaches the reliability can only be set experimentally or estimated during training procedures, which may not match the observed data and often causes degradation of performance. We propose a novel sub-band approach, where frequency sub-bands are multiplied with weighting factors and then combined and converted to cepstra, which have proven to be more robust than both full-band and conventional sub-band cepstra in our experiments. Furthermore, the weighting factors can be estimated by using maximum likelihood adaptation approaches in order to minimize the mismatch between trained models and observed features. We evaluated our methods on AURORA2 and Resource Management tasks and obtained consistent performance improvement on both tasks.
ISSN:0167-6393
1872-7182
DOI:10.1016/j.specom.2005.02.006