Multiple Background Models for Speaker Verification

SESSION 3: Background modeling in Speaker recognition, Forensics

Added: 14. 7. 2010 11:08, Author: Wei-Qiang Zhang, Yuxiang Shan, Jia Liu (Tsinghua University), Length: 0:16:58

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.

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