Bayes factor scoring of GMMs for speaker verification
This paper implements and assesses the Bayes factor as a replacement verification criterion to the likelihood-ratio test in the context of GMM-based speaker verification. An advantage of the Bayesian method is that model parameters are considered random variables, allowing for the incorporation of prior information and uncertainty of parameter estimates into the scoring process. A novel development of Bayes factors for GMMs is presented based on incremental adaptation that is well-suited to inclusion in existing state-of-the-art GMM-UBM systems. Experiments on the 1999 NIST Speaker Recognition Evaluation corpus demonstrate improved performance over expected log-likelihood ratio scoring particularly when combined with the feature mapping technique.
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|Item Type:||Conference Paper|
|Additional Information:||Reproduced in accordance with the copyright policy of the publisher.|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Natural Language Processing (080107)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
|Copyright Owner:||Copyright 2004 International Speech Communication Association (ISCA)|
|Deposited On:||06 Nov 2008|
|Last Modified:||09 Jun 2010 13:06|
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