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

Vaseghi, Saeed V. and Milner, Ben P. (1997) Noise compensation methods for hidden Markov model speech recognition in adverse environments. IEEE Transactions on Speech and Audio Processing, 5 (1). pp. 11-21. ISSN 1063-6676

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Abstract

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.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Depositing User:	EPrints Services
Date Deposited:	01 Oct 2010 13:41
Last Modified:	10 Dec 2024 01:22
URI:	https://ueaeprints.uea.ac.uk/id/eprint/2995
DOI:	10.1109/89.554264

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