Likelihood-ratio empirical kernels for i-vector based PLDA-SVM scoring

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, given a test i-vector and the identity of the target under test, a score vector is constructed by computing the LR scores of the test i-vector with respect to the target-speaker's i-vectors and a set of background-speakers' i-vectors. While in most situations, only one target-speaker i-vector is available for training the SVM, this paper demonstrates that if the enrollment utterance is sufficiently long, a number of target-speaker i-vectors can be generated by an utterance partitioning and resampling technique, resulting in much better scoring SVMs. Results on NIST 2010 SRE suggests that the idea of incorporating background speaker information into PLDA scoring through training speaker-dependent SVMs together with the utterance partitioning techniques can boost the performance of i-vector based PLDA systems significantly.