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Most current state-of-the-art text-independent speaker verification systemstake probabilistic linear discriminant analysis (PLDA) as their backendclassifiers. The parameters of PLDA are often estimated by maximizing theobjective function, which focuses on increasing the value of log-likelihoodfunction, but ignoring the distinction between speakers. In order to betterdistinguish speakers, we propose a multi-objective optimization training forPLDA. Experiment results show that the proposed method has more than 10%relative performance improvement in both EER and MinDCF on the NIST SRE14i-vector challenge dataset, and about 20% relative performance improvement inEER on the MCE18 dataset.

Multiobjective Optimization Training of PLDA for Speaker Verification