A SPEAKER RECOGNITION SYSTEM USING GAUSSIAN MIXTURE MODEL

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 the size of data available for modeling and verifying. Here, the Mel-Scale Frequency Cepstral Coefficient is one of the methods to grab features from wave file of spoken sentences. The Gaussian Mixture Model is a technique applied in the MARF (Modular Audio Recognition Framework) framework to increase outcome estimation. We have presented a Gaussian selection medium for MFCC.