NDLI: Robust speech recognition method based on discriminative learning of environmental features

Content Provider	IEEE Xplore Digital Library
Author	Jiqing Han Munsung Han Gyu-Bong Park Jeongue Park Chengfa Wang
Copyright Year	1999
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Harbin Inst. of Technol., China (Jiqing Han)
Abstract	Learning the influence of additive noise and channel distortions from training data is an effective approach for robust speech recognition. We have proposed a novel method of discriminative learning of environmental features according to minimum classification error (MCE) criterion in previous work, in which additive noise is expressed by the weighted combination of multiple types of noises, and the channel distortions are assumed to consist of the channel distortions of the whole training data and the current utterance. In this paper, we use a Gaussian distribution to stand for the distribution of additive noise, and adaptively learn the combination factors of the channel distortions. The current method is proven better than the former one by experiments.
Starting Page	100
Ending Page	103
File Size	394023
Page Count	4
File Format	PDF
ISBN	0780357396
DOI	10.1109/TENCON.1999.818359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-09-15
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Speech recognition Additive noise Training data Speech enhancement Hidden Markov models Feature extraction Filters Cepstral analysis Mel frequency cepstral coefficient
Content Type	Text
Resource Type	Article

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