WAVELETS FOR SPEAKER RECOGNITION USING GMM CLASSIFIER

Speaker recognition plays an important role in a biometric based identification of the person using the information available in their speech signals. In any speaker recognition system, feature extraction using signal processing approaches is an important stage. In this paper, an efficient speaker recognition system is presented by extracting the energy features of the speech signals using Discrete Wavelet Transform (DWT). Then, the extracted DWT energy features are modeled using Gaussian mixture model (GMM) classifier for the recognition of the speaker. Results prove the efficiency of the speaker recognition system with an accuracy of 96.31% at 4th level DWT features with 16 Gaussian densities.