A temporal warped 2D psychoacoustic modeling for robust speech recognition system
Human auditory system performs better than speech recognition system under noisy condition, which leads us to the idea of incorporating the human auditory system into automatic speech recognition engines. In this paper, a hybrid feature extraction method, which utilizes forward masking, backward masking, and lateral inhibition, is incorporated into mel-frequency cepstral coefficients (MFCC). The integration is implemented using a warped 2D psychoacoustic filter. The AURORA2 database is utilized for testing, and the Hidden Markov Model (HMM) is used for recognition. Comparison is made against lateral inhibition (LI), forward masking (FM), cepstral mean and variance normalization (CMVN), the original 2D psychoacoustic filter and the RASTA filter. Experimental results show that the word recognition rate is significantly improved, especially under noisy conditions.
temporal warping automatic speech recognition 2d mask simultaneous masking temporal masking