SNR-dependent compression of enhanced Mel sub-band energies for compensation of noise effects on MFCC features

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