An auditory model for robust speech recognition

Performance of automatic speech recognition system drops dramatically in the presence of background noise unlike the human auditory system which is more adept at noisy speech recognition. This paper proposes a novel auditory modelling algorithm which is integrated with a feature extraction front-end. The proposed algorithm simulates properties of the human auditory system and applies it to the speech recognition system to enhance its robustness. It integrates simultaneous masking, forward masking and temporal integration effects into traditional mel-frequency cepstral coefficients (MFCC) feature extraction algorithm for robust speech recognition. The proposed method sharpens the power spectrum of the signal in both the frequency domain and the time domain. Experiments carried out on AURORA2 database show that the word recognition rate using our proposed feature extraction method has been effectively increased.