Maximum likelihood sub-band adaptation for robust speech recognition

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Title Maximum likelihood sub-band adaptation for robust speech recognition
Author Zhu, Donglai; Nakamura, Satoshi; Paliwal, Kuldip Kumar; Wang, Renhua
Journal Name Speech Communication
Year Published 2005
Place of publication Amsterdam, The Netherlands
Publisher Elsevier
Abstract 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.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2005 Elsevier : Reproduced in accordance with the copyright policy of the publisher : This journal is available online - use hypertext links
Volume 47
Page from 243
Page to 264
ISSN 0167-6393
Date Accessioned 2006-02-22
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Speech Recognition
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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