Multi-objective Optimization Training of PLDA for Speaker Verification

Most current state-of-the-art text-independent speaker verifi-cation systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers. The parameters of PL-DA are often estimated by maximizing the objective function, which focuses on increasing the value of log-likelihood function, but ignoring the distinction between speakers. In order to better distinguish speakers, we propose a multi-objective optimization training for PLDA. Experiment results show that the proposed method has more than 10% relative performance improvement in both EER and MinDCF on the NIST SRE14 i-vector challenge dataset, and about 20% relative performance improvement in EER on the MCE18 dataset.