NDLI: Recognition of noisy speech using dynamic spectral subband centroids

Content Provider	IEEE Xplore Digital Library
Author	Jingdong Chen Yiteng Huang Qi Li Paliwal, K.K.
Copyright Year	1994
Abstract	Despite their widespread popularity as front-end parameters for speech recognition, the cepstral coefficients derived from either linear prediction analysis or a filter-bank are found to be sensitive to additive noise. In this letter, we discuss the use of spectral subband centroids for robust speech recognition. We show that centroids, if properly selected, can achieve recognition performance comparable to that of the mel-frequency cepstral coefficients (MFCCs) in clean speech, while delivering better performance than MFCC in noisy environments. A procedure is proposed to construct the dynamic centroid feature vector that essentially embodies the transitional spectral information. We discuss some properties of the proposed dynamic features.
Sponsorship	IEEE Signal Processing Society
Starting Page	258
Ending Page	261
Page Count	4
File Size	140842
File Format	PDF
ISSN	10709908
Volume Number	11
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Cepstral analysis Testing Mel frequency cepstral coefficient Working environment noise Speech analysis Noise robustness Automatic speech recognition Hidden Markov models Band pass filters
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing Electrical and Electronic Engineering

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