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

Autor:	Amami, Rimah, Ayed, Dorra Ben, Ellouze, Noureddine
Rok vydání:	2015
Předmět:	Computer Science - Computation and Language Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	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). Comment: 08 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1507.06021 Zobrazit plný text záznamu View this record from Arxiv