In Gaussian mixture model - universal background model (GMM-UBM) speaker verification system, UBM training is the first and the most important stage. However, few investigations have been carried out on how to select suitable training data. In this paper, a VTL-based criterion for UBM training data selection is investigated and a multiple background model (MBM) system is proposed. Experimental results on NIST SRE06 evaluation show that the presented method decreases the equal error rate (EER) of about 8% relatively when compared with the baseline.
Cite as: Zhang, W.-Q., Shan, Y., Liu, J. (2010) Multiple Background Models for Speaker Verification. Proc. The Speaker and Language Recognition Workshop (Odyssey 2010), paper 09
@inproceedings{zhang10_odyssey,
author={Wei-Qiang Zhang and Yuxiang Shan and Jia Liu},
title={{Multiple Background Models for Speaker Verification}},
year=2010,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2010)},
pages={paper 09}
}