An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition
نویسندگان
چکیده
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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ورودعنوان ژورنال:
- CoRR
دوره abs/1507.06021 شماره
صفحات -
تاریخ انتشار 2012