Speaker Verification System Based on the Stochastic Modeling

In this paper we propose a new speaker verification system where the new training and classification algorithms for vector quantization and Gaussian mixture models are introduced. The vector quantizer is used to model sub-word speech components. The code books are created for both training and test utterances. We propose new approaches to normalize distortion of the training and test code books. The test code book quantized over the training code book. The normalization technique includes assigning the equal distortion for training and test code books, distortion normalization and cluster weights. Also the LBG and K-means algorithms usually employed for vector quantization are implemented to train Gaussian mixture models. And finally, we use the information provided by two different models to increase verification performance. The performance of the proposed system has been tested on the Speaker Recognition database, which consists of telephone speech from 8 participants. The additional experiments has been performed on the subset of the NIST 1996 Speaker Recognition database which include .