Phoneme and tonal accent recognition for Thai speech

In 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 c...

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Main Authors: Theera-Umpon N., Chansareewittaya S., Auephanwiriyakul S.
Format: Article
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-79957996410&partnerID=40&md5=9080143e9115cc61e8b2fc89fa732b55
http://cmuir.cmu.ac.th/handle/6653943832/1546
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-15462014-08-29T09:29:27Z Phoneme and tonal accent recognition for Thai speech Theera-Umpon N. Chansareewittaya S. Auephanwiriyakul S. In 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. 2014-08-29T09:29:27Z 2014-08-29T09:29:27Z 2011 Article 9574174 10.1016/j.eswa.2011.04.142 ESAPE http://www.scopus.com/inward/record.url?eid=2-s2.0-79957996410&partnerID=40&md5=9080143e9115cc61e8b2fc89fa732b55 http://cmuir.cmu.ac.th/handle/6653943832/1546 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description In 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.
format Article
author Theera-Umpon N.
Chansareewittaya S.
Auephanwiriyakul S.
spellingShingle Theera-Umpon N.
Chansareewittaya S.
Auephanwiriyakul S.
Phoneme and tonal accent recognition for Thai speech
author_facet Theera-Umpon N.
Chansareewittaya S.
Auephanwiriyakul S.
author_sort Theera-Umpon N.
title Phoneme and tonal accent recognition for Thai speech
title_short Phoneme and tonal accent recognition for Thai speech
title_full Phoneme and tonal accent recognition for Thai speech
title_fullStr Phoneme and tonal accent recognition for Thai speech
title_full_unstemmed Phoneme and tonal accent recognition for Thai speech
title_sort phoneme and tonal accent recognition for thai speech
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-79957996410&partnerID=40&md5=9080143e9115cc61e8b2fc89fa732b55
http://cmuir.cmu.ac.th/handle/6653943832/1546
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