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CSL
2006
Springer

Support vector machines for speaker and language recognition

8 years 8 months ago
Support vector machines for speaker and language recognition
Support vector machines (SVMs) have proven to be a powerful technique for pattern classification. SVMs map inputs into a high dimensional space and then separate classes with a hyperplane. A critical aspect of using SVMs successfully is the design of the inner product, the kernel, induced by the high dimensional mapping. We consider the application of SVMs to speaker and language recognition. A key part of our approach is the use of a kernel that compares sequences of feature vectors and produces a measure of similarity. Our sequence kernel is based upon generalized linear discriminants. We show that this strategy has several important properties. First, the kernel uses an explicit expansion into SVM feature space--this property makes it possible to collapse all support vectors into a single model vector and have low computational complexity. Second, the SVM builds upon a simpler mean-squared error classifier to produce a more accurate system. Finally, the system is competitive and co...
William M. Campbell, Joseph P. Campbell, Douglas A
Added 11 Dec 2010
Updated 11 Dec 2010
Type Journal
Year 2006
Where CSL
Authors William M. Campbell, Joseph P. Campbell, Douglas A. Reynolds, Elliot Singer, Pedro A. Torres-Carrasquillo
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