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Previous studies have demonstrated the benefits of PLDA–SVM scoring with empirical kernel maps for i-vector/PLDA speaker verification. The method not only performs significantly better than the conventional PLDA scoring and utilizes the multiple enrollment utterances of target speakers effectively, but also opens up opportunity for adopting sparse kernel machines in PLDA-based speaker verification...
Gaussian PLDA with uncertainty propagation is effective for i-vector based speaker verification. The idea is to propagate the uncertainty of i-vectors caused by the duration variability of utterances to the PLDA model. However, a limitation of the method is the difficulty of performing length normalization on the posterior covariance matrix of an i-vector. This paper proposes a method to avoid performing...
Previous works have shown the benefits of empirical likelihood ratio (LR) kernels for i-vector/PLDA speaker verification. The method not only utilizes the multiple enrollment utterances of target speakers effectively, but also opens up opportunity for adopting sparse kernel machines for PLDA-based speaker verification systems. This paper proposes taking the advantages of the empirical LR kernels by...
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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