Using vector Taylor series with noise clustering for speech recognition in non-stationary noisy environments

The performance of automatic speech recognizer degrades seriously when there are mismatches between the training and testing conditions. Vector Taylor Series (VTS) approach has been used to compensate mismatches caused by additive noise and convolutive channel distortion in the cepstral domain. In this paper, the conventional VTS is extended by incorporating noise clustering into its EM iteration procedure, improving its compensation effectiveness under non-stationary noisy environments. Recognition experiments under babble and exhibition noisy environments demonstrate that the new algorithm achieves35 % average error rate reduction compared with the conventional VTS.