Support vector machines and Joint Factor Analysis for speaker verification
作者： Najim Dehak , Patrick Kenny , Reda Dehak , Ondrej Glembek , Pierre Dumouchel
DOI: 10.1109/ICASSP.2009.4960564
关键词:
摘要: This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the SVMs are applied different sources of information produced by JFA. These informations Gaussian Mixture Model supervectors speakers Common factors. We found that using SVM in JFA factors gave best results especially when within class covariance normalization method is order compensate channel effect. The new combination comparable other classical scoring techniques.
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