Performance Evaluation of Different Modeling Methods and Classifiers with MFCC and IHC Features for Speaker Recognition

Abstract

Automatic speaker recognition system identifies a person from the information contained in the speech signal. These systems are the most user-friendly means of biometric recognition and are being used in applications like teleconferencing, banking, forensics etc. The accuracy of these depends on the methods used to extract features from the speech signal, modeling methods, classifiers used to identify the speaker and amount of data available for training and testing. In this paper, recognition systems are implemented using both spectro-temporal features and voice-source features. Classification is done with two different classifiers for i-vector method and the accuracy rates are compared.