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Recognition and Rejection Performance in Wordspotting Systems Using Support Vector Machines

Abstract : Support Vector Machines (SVM) is one such machine learning technique that learns the decision surface through a process of discrimination and has a good generalization capacity. SVMs have been proven to be successful classifiers on several classical pattern recogntion problems. In this paper, one of the first applications 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. The Equal Error Rate (EER) for the linear kernel is about 16,34\% compared to 15,23\% obtained by the radial basis function kernel.
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Conference papers
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https://hal.inria.fr/inria-00100833
Contributor : Publications Loria <>
Submitted on : Tuesday, September 26, 2006 - 2:52:14 PM
Last modification on : Friday, May 29, 2020 - 3:02:12 PM

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  • HAL Id : inria-00100833, version 1

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Yassine Benayed, Dominique Fohr, Jean-Paul Haton, Gérard Chollet. Recognition and Rejection Performance in Wordspotting Systems Using Support Vector Machines. 2nd WSEAS International Conference on Signal, Speech and Image Processing - WSEAS ICOSSIP'2002, Sep 2002, Koukounaries, Skiathos Island, Greece, 6 p. ⟨inria-00100833⟩

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