Total: 1
The Mel-frequency cepstral coefficients (MFCC) are commonly used in speech recognition systems. But, they are high sensitive to presence of external noise. In this paper, we propose a noise compensation method for Mel filter bank energies and so MFCC features. This compensation method is performed in two stages: Mel sub-band filtering 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 in place of logarithm function in conventional MFCC feature extraction in presence of additive noise. Results show that the proposed method significantly improves MFCC features performance in noisy conditions where it decreases average word error rate up to 30% for isolated word recognition on three test sets of Aurora 2 database.