Making large-scale support vector machine learning practical
Advances in kernel methods
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Efficient Speaker Recognition Using Approximated Cross Entropy (ACE)
IEEE Transactions on Audio, Speech, and Language Processing
Combining Derivative and Parametric Kernels for Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
Speaker Model Clustering for Efficient Speaker Identification in Large Population Applications
IEEE Transactions on Audio, Speech, and Language Processing
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Speaker identification is the task of determining which speaker characteristics from the speakers known to the system best matches the unknown voice sample. SI requires multiple decision alternatives and to implement SI system using SVM techniques requires multi-class SVM classifier. In this paper, speaker model clustering is implemented on a SVM based SI system. Here, instead of clustering the speakers, we build a SVM classifier which separates a group of speakers. Thus each hyperplane built using SVMs separates a group of speakers and this procedure is repeated in each sub-group until there is only one speaker in each group. Experiments performed on NIST-2002 speech corpus show an improvement in accuracy compared to the conventional multi-class SVM techniques.