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This paper presents the performance of a text independent speaker identification and verification system using Gaussian Mixture Model(GMM).In this paper, we adapted Mel-Frequency Cepstral Coefficients(MFCC) as speaker speech feature parameters and the concept of Gaussian Mixture Model for classification with log-likelihood estimation. The Gaussian Mixture Modeling method with diagonal covariance is increasingly being used for both speaker identification and verification. We have used speakersdoi:10.9790/3021-02861822 fatcat:ipdby35whfhuvk7yyrbxcx3jsu