Cepstral noise subtraction for robust automatic speech recognition

Abstract

The robustness of speech recognizers towards noise can be increased by normalizing the statistical moments of the Mel-frequency cepstral coefficients (MFCCs), e. g. by using cepstral mean normalization (CMN) or cepstral mean and variance normalization (CMVN). The necessary statistics are estimated over a long time window and often, a complete utterance is chosen. Consequently, changes in the background noise can only be tracked to a limited extent which poses a restriction to the performance gain that can be achieved by these techniques. In contrast, algorithms recently developed for single-channel speech enhancement allow to track the background noise quickly. In this paper, we aim at combining speech enhancement techniques and feature normalization methods. For this, we propose to transform an estimate of the noise power spectral density to the MFCC domain, where we subtract it from the noisy MFCCs. This is followed by a conventional CMVN. For background noises that are too instationary for CMVN but can be tracked by the noise estimator, we show that this processing leads to an improvement in comparison to the sole application of CMVN. The observed performance gain emerges especially in low signal-to-noise-ratios.