Speech feature compensation based on pseudo stereo codebooks for robust speech recognition in additive noise environments

In this paper, we propose several compensation approaches to alleviate the effect of additive noise on speech features for speech recognition. These approaches are simple yet efficient noise reduction techniques that use online constructed pseudo stereo codebooks to evaluate the statistics in both clean and noisy environments. The process yields transforms for noisecorrupted speech features to make them closer to their clean counterparts. We apply these compensation approaches on various wellknown speech features, including melfrequency cepstral coefficients (MFCC), autocorrelation melfrequency cepstral coefficients (AMFCC) and perceptual linear prediction cepstral coefficients (PLPCC). Experimental results conducted on the Aurora-2 database show that the proposed approaches provide all types of the features with a significant performance gain when compared to the baseline results and those obtained by using the conventional utterance-based cepstral mean and variance normalization (CMVN).