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

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2 Author(s)
Milner, B.P. ; Sch. of Inf. Syst., East Anglia Univ., Norwich, UK ; Vaseghi, S.V.

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

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Vision, Image and Signal Processing, IEE Proceedings -  (Volume:141 ,  Issue: 5 )