Engineering and Technology Journal

Abstract

This paper presents an effective method for improving the performance of
speaker identification system based on schemes combines the multi
resolution properly of the wavelet transform and radial basis function neural net works (RBFNN), evaluated its performance by comparing the results with other method. The input speech signal is decomposed into L sub band. To capture the characteristic of the vocal tract, the liner prediction code of each (including the linear predictive code (LPC) for full band) are calculated. The radial basis function neural network (RBFNN) approach is used for matching purpose. Experimental results shows that the speaker identification using the methods
achieve (combines the wavelet and RBFNN) give (100%) identification rate
and higher identification rate compared with multi band liner predictive
code, in this paper used Matlab program to prove the results.