An improved model of masking effects for robust speech recognition system

Performance of an 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 modeling algorithm which is integrated into the feature extraction front-end for Hidden Markov Model (HMM). The proposed algorithm is named LTFC which simulates properties of the human auditory system and applies it to the speech recognition system to enhance its robustness. It integrates simultaneous masking, temporal masking and cepstral mean and variance normalization into ordinary 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. Evaluation tests are carried out on the AURORA2 database. Experimental results show that the word recognition rate using our proposed feature extraction method has been effectively increased