Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation

Abdul Rahim Bahari, Aminatuzzaharah Musa, Mohd Zaki Nuawi, Zairi Ismael Rizman, Suziana Mat Saad


This paper discusses the development method to determine the accuracy of pronunciation of the word using global statistical signal analysis parameters. An engineering word that has been chosen is ‘leaching’. The pronunciation of the word ‘leaching’ in the French language has been recorded from 1 native speaker and 4 students. The recording processes use a microphone-laptop system configuration and the signal analyzing processes use MATLAB software. Time and frequency domain plots show a variety of waveforms according to the recorded pronunciation. For data processing, statistical signal analysis parameters involved to extract the signal’s features are kurtosis, root mean square and skewness. The mapping process has been performed to cluster each data. The position of the samples from the students is referred to the samples from the native speaker. The result of the accuracy of the pronunciation of words for each student can be evaluated through the comparison of the position of all the samples. In conclusion, the development of mapping and clustering methods are able to characterize the accuracy of the pronunciation of words.


speech recognition; kurtosis; clustering; skewness; voice signal

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Published by INSIGHT - Indonesian Society for Knowledge and Human Development