J-vector has been proved to be very effective in text-dependent speaker verification with short-duration speech. However, the current state-of-the-art back-end classifiers, e.g. joint Bayesian model, cannot make full use of such deep features. In this paper, we generalize the standard joint Bayesian approach to model the multi-faceted information in the j-vector explicitly and jointly. In our generalization, the j-vector was modeled as a result derived by a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds of latent variables.With DoJoBa, we are able to explicitly build a model that can combine multiple heterogeneous information from the j-vectors. In verification step, we calculated the likelihood to describe whether the two j-vectors having consistent labels or not. On the public RSR2015 data corpus, the experimental results showed that our approach can achieve 0.02\% EER and 0.02\% EER for impostor wrong and impostor correct cases respectively.
Cite as: Shi, Z., Wang, M., Liu, L., Lin, H., Liu, R. (2018) A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification . Proc. The Speaker and Language Recognition Workshop (Odyssey 2018), 365-371, doi: 10.21437/Odyssey.2018-51
@inproceedings{shi18_odyssey,
author={Ziqiang Shi and Mengjiao Wang and Liu Liu and Huibin Lin and Rujie Liu},
title={{A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification }},
year=2018,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2018)},
pages={365--371},
doi={10.21437/Odyssey.2018-51}
}