Deep Neural Network Based Discriminative Training for I-Vector/PLDA Speaker Verification

Abstract

In the studies of i-vector based speaker verification, the discriminative training of probabilistic linear discriminative analysis (PLDA) model has been proven to be an effective way to improve performance. This paper focuses on using a deep neural network (DNN) to strengthen the original discriminatively trained classifiers by its strong capability of nonlinear modeling representation. We first propose a deep neural network based dimensionality reduction model to replace the linear discriminant analysis (LDA) process, and then a discriminative training algorithm is also proposed to jointly optimize the network and PLDA scoring function under single discriminative criterion. Our experiments show that performance improvements are achieved in the male trials of short2-short3 core data set of NIST SRE08.