NDLI: A novel HMM model adaptation and compensation method for robust speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Gengxin Ning Gang Wei
Copyright Year	2005
Description	Author affiliation: Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China (Gengxin Ning; Gang Wei)
Abstract	It is well known that the performance of a speech recognizer, trained with clean speech database, usually degrades drastically when operating in noisy environments. To make it robust to noise, a novel model adaptation method based on the SNR-dependent non-linear spectral compression MFCC features is proposed, which modifies the HMMs of the clean speech to generate the adaptation models on the basis of the estimated Mel-band SNR and the spectral compression coefficients. In this approach, only the clean speech models are adopted, which not only adapt and compensate the means but also modify the variances, hence it can deal with much lower SNR. In addition, this approach can be used in variable noisy environments. Such methods for adaptive approach will be discussed in detail in this paper. By the adoption of the new adaptive approach, substantial improvement can be observed in recognizing in different noisy environments.
Starting Page	284
Ending Page	287
File Size	792799
Page Count	4
File Format	PDF
ISBN	0780395387
DOI	10.1109/ISCIT.2005.1566851
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Degradation Working environment noise Adaptation model Hidden Markov models Speech recognition Speech enhancement Spatial databases Noise robustness Noise generators Mel frequency cepstral coefficient
Content Type	Text
Resource Type	Article

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