NDLI: On the application of hidden Markov models for enhancing noisy speech

Content Provider	IEEE Xplore Digital Library
Author	Ephraim, Y. Malah, D. Juang, B.-H.
Copyright Year	1988
Description	Author affiliation: AT&T Bell Lab., Murray Hill, NJ, USA (Ephraim, Y.)
Abstract	An algorithm is proposed for enhancing noisy speech which has been degraded by statistically independent additive noise. The algorithm is based on modeling the clean speech as a hidden Markov process with mixtures of Gaussian autoregressive (AR) output processes and modeling the noise as a sequence of stationary, statistically independent, Gaussian AR vectors. The parameter sets of the models are estimated using training sequences from the clean speech and the noise process. The parameter set of the hidden Markov model is estimated by the segmental k-means algorithm. Given the estimated models, the enhancement of the noisy speech is done by alternate maximization of the likelihood function of the noisy speech, one over all sequences of states and mixture components assuming that the clean speech signal is given, and then over all vectors of the original speech using the resulting most probable sequence of states and mixture components. This alternating maximization is equivalent to first estimating the most probable sequence of AR models for the speech signal using the Viterbi algorithm, and then applying these AR models for constructing a sequence of Wiener filters which are used to enhance the noisy speech.<>
Starting Page	533
Ending Page	536
File Size	434053
Page Count	4
File Format	PDF
ISSN	15206149
DOI	10.1109/ICASSP.1988.196638
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1988-04-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech enhancement Speech processing State estimation Additive noise Signal processing Parameter estimation Maximum likelihood estimation Iterative algorithms Degradation
Content Type	Text
Resource Type	Article

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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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