Support Vector Machines is a new and promising technique in
statistical learning theory. Recently, this technique produced very
interesting results in pattern recognition.

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.