Robust Text Independent Speaker Identification Using Hybrid GMM-SVM System

Abstract

This paper introduces and motivates the use of the statistical method Gaussian Mixture Model (GMM) and Support Vector Machines (SVM) for robust textindependent speaker identification. Features are extracted from the dialect DR1 of the Timit corpus. They are presented by MFCC, energy, Delta and Delta-Delta coefficients. GMM is used to model the feature extractor of the input speech signal and SVM is used for handling the task of decision making. The SVM is trained using inputs, which are the feature vectors presented by the GMM. Our results prove that the hybrid GMM-SVM system is significantly more preferment than the SVM system. We report improvements of 85,37% amelioration in identification rate compared to the SVM identification rate.