NDLI: Discriminative approach to dynamic variance adaptation for noisy speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Delcroix, M. Watanabe, S. Nakatani, T. Nakamura, A.
Copyright Year	2011
Description	Author affiliation: NTT Communication Science Laboratories, NTT corporation, 2-4, Hikaridai, Seika-cho (Keihanna Science City), Soraku-gun, Kyoto 619-0237 Japan (Delcroix, M.; Watanabe, S.; Nakatani, T.; Nakamura, A.)
Abstract	The performance of automatic speech recognition suffers from severe degradation in the presence of noise or reverberation. One conventional approach for handling such acoustic distortions is to use a speech enhancement technique prior to recognition. However, most speech enhancement techniques introduce artifacts that create a mismatch between the enhanced speech features and the acoustic model used for recognition, therefore limiting the improvement in recognition performance. Recently, there has been increased interest in methods capable of compensating for such a mismatch by accounting for the feature variance during decoding. In this paper, we propose to estimate the feature variance using an adaptation technique based on a discriminative criterion. In an experiment using the Aurora2 database, the proposed method could achieve significant digit error rate reduction compared with a spectral subtraction pre-processor, and using a discriminative criterion for adaptation provided further improvement compared with maximum likelihood estimation.
Starting Page	7
Ending Page	12
File Size	303379
Page Count	6
File Format	PDF
ISBN	9781457709975
e-ISBN	9781457709999
DOI	10.1109/HSCMA.2011.5942414
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-30
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Model Adaptation Noise Noise reduction Variance Compensation Hidden Markov models Speech recognition Speech enhancement MMI Speech Acoustics Noise measurement Robust ASR
Content Type	Text
Resource Type	Article

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