Practical Selection of SVM Supervised Parameters with Different Feature Representations for Vowel Recognition
This work addresses parameter tuning for SVM in speech recognition, but it is incremental as it applies existing methods to a specific dataset.
The study tackled the problem of selecting SVM parameters and feature representations for vowel recognition, finding that performance varies significantly with different kernels and parameters, achieving up to 85% accuracy with optimal settings on the TIMIT corpus.
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