| This thesis compares several speaker recognition algorithms in reverberant environments. The Gaussian Mixture Model, Auto-regressive vector model, covariance based models and Multi layer perceptron are compared.; The methods are compared when there is a mismatch between the training and test speech due to the non-reverberant nature of the training speech and the reverberant nature of the test speech.; In order to counteract the effects of reverberation, training was performed using reverberant speech. Average recognition accuracy improved by 9.8% for the GMM, 53% for AR-Itakura, 18.8% for sphericity measure, 18.1% for the divergence shape measure and 15.9% for AR-AGS.; A method was proposed to create a set of reverberant models for each speaker using speech reverberated to different degrees. A novel technique was proposed to determine which reverberant model for each speaker best matches the reverberant test speech. 98.7% classification accuracy was obtained using an Auto-regressive vector model adapted for this purpose. |
