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Estimation of Relative Operating Characteristics of Text Independent Speaker Verification

This paper presents the performance of a text independent speaker 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 verification. We have used speakers in experiments, modeled with 13 mel-cepstral coefficients. Speaker verification performance was conducted using False Acceptance Rate (FAR), False Rejection Rate(FRR) and Equal Error Rate(ERR)or Relative Operating Characteristics.