  It is known that the classification performance of Support Vector Machine
(SVM) can be conveniently affected by the different parameters of the kernel
tricks and the regularization parameter, C. Thus, in this article, we propose a
study in order to find the suitable kernel with which SVM may achieve good
generalization performance as well as the parameters to use. We need to analyze
the behavior of the SVM classifier when these parameters take very small or
very large values. The study is conducted for a multi-class vowel recognition
using the TIMIT corpus. Furthermore, for the experiments, we used different
feature representations such as MFCC and PLP. Finally, a comparative study was
done to point out the impact of the choice of the parameters, kernel trick and
feature representations on the performance of the SVM classifier
