An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition

Rimah Amami; Dorra Ben Ayed; Noureddine Ellouze

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arXiv · 1507.06021

An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition

Rimah Amami·Dorra Ben Ayed·Noureddine Ellouze

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Abstract

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).

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Rimah Amami, Dorra Ben Ayed, Noureddine Ellouze. 2015-07-22. An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition. https://arxiv.org/abs/1507.06021

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