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Recently submitted ICASSP paper - Improving Out-domain PLDA Speaker Verification using Unsupervised Inter-dataset Variability Compensation Approach

posted Oct 10, 2014, 12:25 PM by Ahilan M.K
Recently researchers have found that that when the state-of-the-art probabilistic linear discriminant analysis (PLDA) speaker verification systems are trained using out-domain data, it significantly affects speaker verification performance due to the mismatch between development data and evaluation data. To overcome this problem we propose a novel unsupervised inter dataset variability (IDV) compensation approach to compensate the dataset mismatch. This research outcome was submitted to ICASSP 2015 conference.
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