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

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.