Auditory Perception Based Admissible Wavelet Packet Trees For Speech Recognition

This paper presents the use of auditory perception based admissible wavelet packet tree (WPT) for partitioning of speech frequencies into different bands based on the Mel scale or the Bark Scale. The proposed WPTs selected using root mean square error (RMSE) criterion mimic the Mel scale or the bark scale more accurately and hence the human auditory system. Performance of the features obtained from the proposed WPTs is compared with Mel frequency cepstral coefficients (MFCC). The algorithms are evaluated using NIST TI-46 isolated-word database using hidden Markov model (HMM) as a classifier. Experimental results show that the performance of proposed features is better than MFCC and other wavelet features for isolated word recognition (IWR).