Comparison of some noise compensation methods for speech recognition in adverse environments

Milner, Ben and Vaseghi, Saeed V. (1994) Comparison of some noise compensation methods for speech recognition in adverse environments. Proceedings of the IEE on Vision, Image and Signal Processing, 141 (5). pp. 280-288.

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Abstract

A comparative study is presented of three noise-compensation schemes, namely spectral subtraction, Wiener filters, and noise adaptation, for hidden-Markov-model-based speech recognition in adverse environments. The noise-compensation methods are evaluated on a spoken-digit database, in the presence of car noise and helicopter noise at different signal-to-noise ratios. Experimental results demonstrate that the noise-compensation methods achieve a substantial improvement in recognition accuracy across a wide range of signal-to-noise ratios. At a signal-to-noise ratio of -6 dB the recognition accuracy is improved from 11% to 83%. The use of cepstral-time matrices as an improved speech representation is also considered, and their combination with the noise-compensation methods is shown. Experiments show that the cepstral-time matrix is a more robust feature than a vector of identical size, 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:	LivePure Connector
Date Deposited:	09 Jan 2024 01:38
Last Modified:	10 Dec 2024 01:43
URI:	https://ueaeprints.uea.ac.uk/id/eprint/94112
DOI:	10.1049/ip-vis:19941303

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