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method does not fully utilize the discriminative information available in the
training data. To fully harness the discriminative information, this paper
proposes training a support vector machine (SVM) for computing the verification
scores. More precisely, the models of target speakers, individual background
speakers, and claimants are converted to AF-supervectors, which form the inputs
to an AF-based kernel of the SVM for computing verification scores. Results
show that the proposed AF-kernel scoring is complementary to likelihood-ratio
scoring, leading to better performance when the two scoring methods are
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