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Speaker Verification Using Support Vector Machines and High-Level Features

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5 Author(s)
Campbell, W.M. ; Massachusetts Inst. of Technol., Lexington ; Campbell, J.P. ; Gleason, T.P. ; Reynolds, D.A.
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High-level characteristics such as word usage, pronunciation, phonotactics, prosody, etc., have seen a resurgence for automatic speaker recognition over the last several years. With the availability of many conversation sides per speaker in current corpora, high-level systems now have the amount of data needed to sufficiently characterize a speaker. Although a significant amount of work has been done in finding novel high-level features, less work has been done on modeling these features. We describe a method of speaker modeling based upon support vector machines. Current high-level feature extraction produces sequences or lattices of tokens for a given conversation side. These sequences can be converted to counts and then frequencies of n-gram for a given conversation side. We use support vector machine modeling of these n-gram frequencies for speaker verification. We derive a new kernel based upon linearizing a log likelihood ratio scoring system. Generalizations of this method are shown to produce excellent results on a variety of high-level features. We demonstrate that our methods produce results significantly better than standard log-likelihood ratio modeling. We also demonstrate that our system can perform well in conjunction with standard cesptral speaker recognition systems.

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Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:15 ,  Issue: 7 )