COMPARISON OF FIVE CLASSIFIERS FOR CLASSIFICATION OF SYLLABLES SOUND USING TIME-FREQUENCY FEATURES

In a speech recognition and classification system, the step of determining the suitable and reliable classifier is essential in order to obtain optimal classification result. This paper presents Indonesian syllables sound classification by a C4.5 decision tree, a Naive Bayes classifier, a Sequential...

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Bibliographic Details
Published inJournal of engineering science & technology Vol. 13; no. 9; pp. 2964 - 2977
Main Authors DOMY KRISTOMO, RISANURI HIDAYAT, INDAH SOESANTI
Format Journal Article
LanguageEnglish
Published Taylor's University 01.09.2018
Subjects
C4.5
Feature extraction
Multi-layer perceptron
Naive bayes
Random forest
SMO
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Summary:In a speech recognition and classification system, the step of determining the suitable and reliable classifier is essential in order to obtain optimal classification result. This paper presents Indonesian syllables sound classification by a C4.5 decision tree, a Naive Bayes classifier, a Sequential Minimal Optimization (SMO) algorithm, a Random Forest decision tree, and a Multi-Layer Perceptron (MLP) for classifying twelve classes of syllables. This research applies five different features set, those are combination features of Discrete Wavelet Transform (DWT) with statistical denoted as WS, the Renyi Entropy (RE) features, the combination of Autoregressive Power Spectral Density (AR-PSD) and Statistical denoted as PSDS, the combination of PSDS and the selected features of RE by using Correlation-Based Feature Selection (CFS) denoted as RPSDS, and the combination of DWT, RE, and AR-PSD denoted as WRPSDS. The results show that the classifier of MLP has the highest performance when it is combined with WRPSDS.
ISSN:1823-4690