High-level speaker verification via articulatory-feature based sequence kernels and SVM

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 performance when the two scoring methods are combined. Further performance enhancement was also observed when the AF scores were combined with acoustic scores derived from a GMM-UBM system.