Noise compensation methods for hidden Markov model speech recognition in adverse environments

Saeed V. Vaseghi, Ben P. Milner

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94 Citations (Scopus)


Several noise compensation schemes for speech recognition in impulsive and nonimpulsive noise are considered. The noise compensation schemes are spectral subtraction, HMM-based Wiener (1949) filters, noise-adaptive HMMs, and a front-end impulsive noise removal. The use of the cepstral-time matrix as an improved speech feature set is explored, and the noise compensation methods are extended for use with cepstral-time features. Experimental evaluations, on a spoken digit database, in the presence of ear noise, helicopter noise, and impulsive noise, demonstrate that the noise compensation methods achieve substantial improvement in recognition across a wide range of signal-to-noise ratios. The results also show that the cepstral-time matrix is more robust than a vector of identical size, which is composed of a combination of cepstral and differential cepstral features.
Original languageEnglish
Pages (from-to)11-21
Number of pages11
JournalIEEE Transactions on Speech and Audio Processing
Issue number1
Publication statusPublished - Jan 1997

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