TESPAR Feature Based Isolated Word Speaker Recognition System

This paper presents a time domain feature extraction method of speaker identification using Time Encoded Signal Processing and Recognition (TESPAR) approach. TESPAR matrices are not only generated for English words but also for the Urdu and Pashto words. For classification, the standard Artificial Neural Network (ANN) classifier and its variant have been used. The recognition results obtained show that when the user spoke a word from the vocabulary in an isolated fashion, 99% of the time it is correctly recognized. The results of TESPAR based feature are compared with features extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coefficients (LPC). The MFCC and LPC features are obtained using the Hidden Markov Model toolkit, HTK. Feed forward neural network with back propagation has been used for the recognition. The results show that the speaker recognition systems with TESPAR features gives better performance with a high recognition rate and low computational complexity as compared with MFCC and LPC based features.