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\begin{abstract}\vspace{-0.06cm}
Articulatory-feature based pronunciation models (AFCPMs) are capable of
capturing the pronunciation variations among different speakers and are good
for high-level speaker recognition. However, the likelihood-ratio scoring
method of AFPCMs is based on a decision boundary created by training the target
speaker model and universal background model (UBM) separately. Therefore, the
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......

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Approximate Pages: 17 (250 words per double-spaced page)

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