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dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorSuppakarn Chansareewittayaen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.description.abstractIn this paper, we investigate the application of a phoneme recognition system with a soft phoneme segmentation procedure for Thai speech. In addition, we propose a new method to classify the tonal accent of a syllable. The recognition system classifies Thai phonemes, including the 21-class initial consonants, the 18-class vowels, and the 9-class final consonants, using discrete hidden Markov models. Two features, i.e., the Mel frequency with perceptual linear prediction and the Mel frequency cepstrum coefficients, are compared to investigate their utilities in phoneme recognition. Neural networks are applied to classify the 5-class tonal accents by using the temporal variation of pitch frequencies across syllables as features. Speaker-dependent and speaker-independent data sets recorded from 30 speakers are used to test our recognition system. The experimental results show promising recognition performances for the phonemes and tonal accents in both data sets. © 2010 Elsevier Ltd. All rights reserved.en_US
dc.subjectComputer Scienceen_US
dc.titlePhoneme and tonal accent recognition for Thai speechen_US
article.title.sourcetitleExpert Systems with Applicationsen_US
article.volume38en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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