2D psychoacoustic filtering for robust speech recognition

One of the weaknesses of speech recognition system is its lack of robustness to background noise as compared to human listeners under similarly conditions. This paper proposes a 2D psychoacoustic modeling algorithm which is integrated with a feature extraction front-end for hidden Markov model (HMM). The proposed algorithm incorporates the properties of human auditory system and applies it to the speech recognition system to enhance its robustness. It integrates forward masking, lateral inhibition and cepstral mean normalization into ordinary mel-frequency cepstral coefficients (MFCC) feature extraction algorithm. Experiments carried out on AURORA2 database show that the word recognition rate can be improved significantly at low computational cost.