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Conventional PLDA scoring in i-vector speaker verification involves the i-vectors of target speakers and claimants only. We have previously demonstrated that better performance can be achieved by incorporating the information of background speakers in the scoring process via speaker-dependent SVMs. This is achieved by defining a PLDA score space with dimension equal to the number of training i-vectors...
Likelihood ratio (LR) scoring in PLDA speaker verification systems only uses the information of background speakers implicitly. This paper exploits the notion of empirical kernel maps to incorporate background speaker information into the scoring process explicitly. This is achieved by training a scoring SVM for each target speaker based on a kernel in the empirical feature space. More specially,...
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