Abstract
Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1],[2],[3].
In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising.
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© 2002 Springer-Verlag Berlin Heidelberg
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Ayed, Y.B., Fohr, D., Haton, J.P., Chollet, G. (2002). Keyword Spotting Using Support Vector Machines. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2002. Lecture Notes in Computer Science(), vol 2448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46154-X_39
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DOI: https://doi.org/10.1007/3-540-46154-X_39
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44129-8
Online ISBN: 978-3-540-46154-8
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