NDLI: On noise estimation for robust speech recognition using vector Taylor series

Content Provider	IEEE Xplore Digital Library
Author	Yong Zhao Biing-Hwang Juang
Copyright Year	2010
Description	Author affiliation: Center for Signal and Image Processing, Georgia Institute of Technology, USA (Yong Zhao; Biing-Hwang Juang)
Abstract	In this paper, we propose a novel noise variance estimation method using the fixed point method for the VTS-based robust speech recognition. Noise parameters are re-estimated over a given utterance using an EM algorithm. The derivative of the auxiliary function with respect to the noise variance is resolved, and the fixed point algorithm estimates the noise variance by recursively approximating the root of the resulting derivative. The method leads to a re-estimation formula with a flavor like the standard ML variance estimation, and the iteration procedure is step-size free. We also investigate improving the noise estimation for efficient VTS adaptation. Several fast noise estimation methods are examined including estimation from non-speech areas and incremental adaptation. In the evaluation over Aurora 2 database, the proposed noise variance estimation method obtains a significant improvement in recognition accuracy over the method using sample variance. Further experiments show that the VTS ML estimation over non-speech areas is an effective fast adaptation method. The final refined approach achieves 8.75% WER, 13% relative improvement over the conventional VTS adaptation.
Starting Page	4290
Ending Page	4293
File Size	130744
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495669
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Speech recognition Taylor series Working environment noise Acoustic noise Additive noise Speech enhancement Maximum likelihood estimation Vectors Gaussian noise noise estimation Robust speech recognition vector Taylor series
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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