Improving speaker verification using MFCC order

Abstract

This paper presents a text-dependent speaker verification using Mel-Frequency Cepstral Coefficients (MFCC) and Support Vector Machine (SVM). Mel-Frequency Cepstral Coefficients technique has been used to extract the characteristic from the recorded voice spoken by the user and SVM is used to classify the all models of the speakers and impostors. A Malay spoken digit database is utilized for the training and testing. The aim of this paper is to improve the performance of SVM by selecting the best order of Mel-Frequency Cepstral Coefficients. Five types of Mel-Frequency Cepstral Coefficients order (5, 10, 15, 20, 25) have been tested and classified using SVM. It is shown that 20th and 25th order of MFCC achieved the best total success rate (TSR) and Equal Error Rate (EER).