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

Ben Milner, Saeed V. Vaseghi

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7 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)280-288
JournalProceedings of the IEE on Vision, Image and Signal Processing
Issue number5
Publication statusPublished - Oct 1994

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