  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).
