A framework for robust MFCC feature extraction using SNR-dependent compression of enhanced mel filter bank energies

The Mel-frequency cepstral coefficients (MFCC) are most widely used and successful features for speech recognition. But, their performance degrades in presence of additive noise. In this paper, we propose a noise compensation method for Mel filter bank energies and so MFCC features. This compensation method includes two steps: Mel sub-band spectral subtraction and then compression of Mel-Sub-band energies. In the compression step, we propose a sub-band SNR-dependent compression function. We use this function instead of logarithm function in conventional MFCC feature extraction in presence of additive noise. Experimental results show that the proposed method significantly improves MFCC features performance in noisy conditions where it decreases word error rate about 70% in SNR value of 0 dB for different types of additive noise.