Speaker recognition in reverberant environments
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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 Autoregressive vector model adapted for this purpose.
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Copyright © 2005 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2005
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gammal-speakerrecognitioninreverberantenvironments.pdf | 2023-05-03 | Public | Download |