  Speaker verification systems usually suffer from the mismatch problem between
training and evaluation data, such as speaker population mismatch, the channel
and environment variations. In order to address this issue, it requires the
system to have good generalization ability on unseen data. In this work, we
incorporate Bayesian neural networks (BNNs) into the deep neural network (DNN)
x-vector speaker verification system to improve the system's generalization
ability. With the weight uncertainty modeling provided by BNNs, we expect the
system could generalize better on the evaluation data and make verification
decisions more accurately. Our experiment results indicate that the DNN
x-vector system could benefit from BNNs especially when the mismatch problem is
severe for evaluations using out-of-domain data. Specifically, results show
that the system could benefit from BNNs by a relative EER decrease of 2.66% and
2.32% respectively for short- and long-utterance in-domain evaluations.
Additionally, the fusion of DNN x-vector and Bayesian x-vector systems could
achieve further improvement. Moreover, experiments conducted by out-of-domain
evaluations, e.g. models trained on Voxceleb1 while evaluated on NIST SRE10
core test, suggest that BNNs could bring a larger relative EER decrease of
around 4.69%.
