Comparison of Text Independent Speaker Identification Systems using GMM and i-Vector Methods

Voice biometric provides a person's unique identity and hence is widely employed for security applications. Here, Text Independent Speaker Recognition system is implemented using Gaussian Mixture Models (GMM) and i-Vector method with two features PNCC (Power Normalized Cepstral Coefficients) and RASTA PLP (Relative Spectral Perceptual Linear Prediction) coefficients. It was observed that accuracy for speaker identification is better when pitch and formants are appended to basic features. Also, the accuracy of i-vector method with PLDA (Probabilistic Linear Discriminant Analysis) classifier is better than that with CDS (Cosine Distance Scoring) classifier. Further, the performance improves when longer utterances are used.