Score calibrating for speaker recognition based on support vector machines and Gaussian Mixture Models
Marcel Katz, Martin Schafföner, Sven E. Krüger, Andreas Wendemuth · 2007
In this paper we investigate three approaches of calibrating and fusing output scores for speaker verification. Today’s speaker recognition systems often consist of several subsystems that use different generative and discriminative classifiers. If subsystems like Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs) are used to obtain a final score for decision a probabilistic calibration of single classifier scores is important. Experiments on the NIST 2006 evaluation dataset show a performance improvement compared to the single subsystems and the standard un-calibrated fusion methods. KEY WORDS score calibration, score fusion, speaker recognition, support vector machine 1