HMM-based Strategies for Enhancement of Speech Signals Embedded in Nonstationary Noise

H. Sameti, H. Sheikhzadeh, Li Deng, and R. Brennan

Abstract

An improved hidden Markov model-based (HMMbased)

speech enhancement system designed using the minimum

mean square error principle is implemented and compared with

a conventional spectral subtraction system. The improvements

to the system are: 1) incorporation of mixture components in

the HMM for noise in order to handle noise nonstationarity

in a more flexible manner, 2) two efficient methods in the

speech enhancement system design that make the system realtime

implementable, and 3) an adaptation method to the noise

type in order to accommodate a wide variety of noises expected

under the enhancement system’s operating environment. The

results of the experiments designed to evaluate the performance of

the HMM-based speech enhancement systems in comparison with

spectral subtraction are reported. Three types of noise—white

noise, simulated helicopter noise, and multitalker (cocktail party)

noise—were used to corrupt the test speech signals. Both objective

(global SNR) and subjective mean opinion score (MOS) evaluations

demonstrate consistent superiority of the HMM-based

enhancement systems that incorporate the innovations described

in this paper over the conventional spectral subtraction method.

Details

Publication typeArticle
Published inIEEE Trans. on Speech and Audio Processing
Pages445-455
Volume6
Number5
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