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Mel Frequency Cepstral Coefficients for Speaker Recognition Using Gaussian Mixture Model-Artificial Neural Network Model

Speaker Recognition (SP) is a topic of great significance in areas of intelligent and security. In Biometric SP using automated method of verifying or recognizing the identity of the person on the basis of some application, such as a finger print or face pattern and human voice. Many method have been proposed in the literature are focusing on front end processing such as PLP and LPC. In this paper, we study the applicability of Artificial Neural Network (ANNs) as core classifiers and Gaussian Mixture Mode (GMMs) for Mel Frequency Cepstral Coefficients (MFCC). Two different approaches have been compared. The GMMs commonly used in many application domains firstly review. We also applied a sampled method for speaker recognition that is based on ANNs. The experiment result shows that the Gaussian Mixture Model achieved highest accuracy than ANN model. However, GMM despite certain disadvantages they present mainly at the training stage, the Artificial Neural Network show better performance for speech and need less training data than the GMM-based ones [1]. It is assumed that hybrid of both models will perform better and merit for further development.