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Integrating a joint Bayesian generative model in a discriminative learning framework for speaker verification

2021-01-09 10:10:04
Xugang Lu, Peng Shen, Yu Tsao, Hisashi Kawai

Abstract

tract: The task for speaker verification (SV) is to decide an utterance is spoken by a target or imposter speaker. In most SV studies, a log-likelihood ratio (L_LLR) score is estimated based on a generative probability model on speaker features, and compared with a threshold for decision making. However, the generative model usually focuses on feature distributions and does not have the discriminative feature selection ability, which is easy to be distracted by nuisance features. The SV, as a hypothesis test, could be formulated as a binary classification task where a neural network (NN) based discriminative learning could be applied. Through discriminative learning, the nuisance features could be removed with the help of label supervision. However, the discriminative learning pays more attention to classification boundaries which is prone to overfitting to training data and yielding poor generalization on testing data. In this paper, we propose a hybrid learning framework, i.e., integrating a joint Bayesian (JB) generative model into a neural discriminative learning framework for SV. A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in a pair-wised discriminative learning. By initializing the Siamese NN with the parameters learned from the JB model, we further train the model parameters with the pair-wised samples as a binary discrimination task. Moreover, direct evaluation metric in SV, i.e., minimum empirical Bayes risk, is designed and integrated as an objective function in the discriminative learning. We carried out SV experiments on speakers in the wild (SITW) and Voxceleb corpora. Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-the-art models for SV.

Abstract (translated)

URL

https://arxiv.org/abs/2101.03329

PDF

https://arxiv.org/pdf/2101.03329


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