Fractional Fourier transform based features for speaker recognition using support vector machine

Abstract

This paper presents a text-independent speaker recognition technique in which the conventional Fourier transform in Mel-Frequency Cepstral Coefficient (MFCC) front-end is substituted by fractional Fourier transform. Support Vector Machine (SVM) maps these input features into a high-dimensional space to separate classes by a hyperplane with enhanced discrimination capability. SVM based on mean-squared error classifier produces more accurate system. The Fractional Fourier Transform (FrFT) reveals the mixed time and frequency components of the signal. Modelling of speech signals as mixed time and frequency signals represents better production and perception speech characteristics. Processing of time-varying signals in fractional Fourier domain allows us to estimate the signal with least Mean Square Error (MSE) making the technique robust against additive noise compared to Fourier domain maintaining same computational complexity. The feasibility of the proposed technique has been tested experimentally using Texas Instruments and Massachusetts Institute of Technology (TIMIT) and Shri Guru Gobind Singhji (SGGS) databases. The experimental results show the superiority of the proposed method.