New scheme based on GMM-PCA-SVM modelling for automatic speaker recognition

Most of the existing speaker recognition systems are based on the basic GMM, the state of the art GMM-UBM, the SVM or more recently the GMM-SVM modeling. In this paper, a new scheme for Automatic Speaker Recognition (ASR), namely GMM-PCA-SVM, is presented. Dimensionality reduction using Principal Component Analysis (PCA) technique, which was previously applied in the front-end process, is now incorporated in the core of the GMM-SVM modeling part, in order to reduce the size of the adapted means vectors issued from the Universal Background Model (UBM). A Comparative study, using Mel Frequency Cepstral Coefficients (MFCC) with Cepstral Mean Subtraction (CMS) extracted from the TIMIT database is performed for speaker recognition in clean and noisy environments. It is shown that the proposed scheme is a promising way for the ASR task. In fact, the recognition performances using GMM-PCA-SVM proposed method is significantly improved compared to the conventional SVM or GMM-SVM based systems.