Auditory model based speech feature extraction and its application to speaker identification

According to the characteristics of the auditory periphery and cochlear nucleus, as well as attempting to simulate the mechanism of auditory system as a whole, two kinds of novel speech feature are presented in this paper, and a framework of neural network has been adopted. The two features considered are: the weighted average localized synchronized rate cepstrum, and the weighted firing rate cepstrum. Both of them are applied to speaker identification. The modular tree and modified linear opinion pools are used as classifiers to simulate the parallel processing mechanism of the upper level function of auditory system. Good recognition accuracy is obtained under both clean and noisy environments