An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition
This is an incremental study that benchmarks existing methods for vowel recognition, relevant for speech processing researchers.
The paper compared the accuracy of five supervised learning classifiers and two combined classifiers for vowel recognition using the TIMIT Corpus and MFCCs, finding performance differences among SVM, KNN, Naive Bayes, QDC, and Nearest Mean.
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).