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An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition by Amami, Rimah; Ayed, Dorra Ben; Ellouze, Noureddine is a scholarly article available to read on EtoBox.

What is An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition about?

In this article, we conduct a study on the performance of some supervised learning algorithms for vowel recognition. This study aims to compare the accuracy of each algorithm. Thus, we present an empirical comparison between five supervised learning classifiers and two combined classifiers: SVM, KNN, Naive Bayes, Quadratic Bayes Normal (QDC) and Nearst Mean. Those algorithms were tested for vowel recognition using TIMIT Corpus and Mel-frequency cepstral coefficients (MFCCs).

Author

Amami, Rimah; Ayed, Dorra Ben; Ellouze, Noureddine

Published

2015

Language

EN