Odyssey 2010: The Speaker and Language Recognition Workshop
Brno, Czech Republic
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.
Full Paper (PDF)
Bibliographic reference. Zhang, Wei-Qiang / Shan, Yuxiang / Liu, Jia (2010): "Multiple Background Models for Speaker Verification", In Odyssey-2010, paper 009.