Classification of the syllables sound using wavelet, Renyi entropy and AR-PSD features

Abstract

Feature extraction plays a very important role in the speech classification process because a better feature is good for improving the classification rate. This paper presents a speech feature extraction method by using Discrete Wavelet Transform (DWT) at 7th level of decomposition with mother wavelet of Dau-bechies 2, Renyi Entropy (RE), Autoregressive Power Spectral Density (AR-PSD), Statistical, as well as the combination of each method for extracting and classifying the certain Indonesian velar-vowel and alveolar-vowel syllables. Five different features set used in this study, namely the combination features of DWT and statistical (WS), RE, the combination of AR-PSD and Statistical (PSDS), the combination of PSDS and the selected features of RE (RPSDS), and the combination of DWT, RE, and AR-PSD (WRPSDS). Each syllable is segmented at a certain length to form a consonant-vowel. Multi-layer perceptron is used as a classifier after feature extraction process. The results show that the rank of the average recognition rate are WRPSDS, WS, RPSDS, PSDS, and RE, respectively.