The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Recent Advances in Speaker Recognition (Invited Paper)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Robust ASR using Support Vector Machines
Speech Communication
A new approach for spoken language identification based on sequence kernel SVMs
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
SVMs for automatic speech recognition: a survey
Progress in nonlinear speech processing
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Generative Gaussian Mixture Models (GMMs) are known to be the dominant approach for modeling speech sequences in text independent speaker verification applications because of their scalability, good performance and their ability in handling variable size sequences. On the other hand, because of their discriminative properties, models like Support Vector Machines (SVMs) usually yield better performance in static classification problems and can construct flexible decision boundaries. In this paper, we try to combine these two complementary models by using Support Vector Machines to postprocess scores obtained by the GMMs. A cross-validation method is also used in the baseline system to increase the number of client scores in the training phase, which enhances the results of the SVM models. Experiments carried out on the XM2VTS and PolyVar databases confirm the interest of this hybrid approach.