Practical Selection of SVM Supervised Parameters with Different Feature Representations for Vowel Recognition

TL;DR

This paper investigates how different SVM parameters and feature representations like MFCC and PLP affect vowel recognition performance on the TIMIT corpus, aiming to optimize classifier generalization.

Contribution

It provides a practical analysis of parameter selection and feature impact on SVM performance for vowel recognition tasks.

Findings

Optimal kernel and parameters improve recognition accuracy

Feature choice significantly affects SVM performance

Parameter sensitivity varies with feature representation

Abstract

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