Features Based on Fourier-Bessel Expansion for Application of Speaker Identification System

A compact representation of speech is possible using Bessel functions because of the similarity between voiced speech and the Bessel functions. Both voiced speech and the Bessel functions exhibit quasiperiodicity and decaying amplitude with time. In this paper, we have developed various feature extraction techniques using zero-order Bessel functions as basis functions for the task of closed-set textindependent speaker identification system. The features are tested on TIMIT, CHAINS and IIIT-Hyderabad speech databases. The performance of the proposed feature extraction techniques is compared with the results obtained using MFCC features. A generic Gaussian Mixture Model (GMM) classification system is used for speaker modeling. The proposed extraction techniques provide results comparable to the widely used MFCC.