Discrete Fractional Fourier Transform and Vector Quantization Based Speaker Identification System

In the study of speaker identification, Mel Frequency Cepstral Coefficient (MFCC) method is the best and most popular which is used to feature extraction. Further Vector Quantization (VQ) technique is used to minimize the amount of data to be handled and mapping vectors from a large vector space to a finite number of regions in that space in recent years. Voice, like other biometrics, cannot be forgotten or misplaced, unlike knowledge-based (e.g., password) or possession-based (e.g., key) access control methods. In the present work, modified Mel frequency cepstral coefficients using discrete fractional Fourier transform and vector quantization is obtained. The experimental results are analyzed with the help of MATLAB.