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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nipon Theera-Umpon | en_US |
dc.contributor.author | Suppakarn Chansareewittaya | en_US |
dc.contributor.author | Sansanee Auephanwiriyakul | en_US |
dc.date.accessioned | 2018-09-10T04:03:22Z | - |
dc.date.available | 2018-09-10T04:03:22Z | - |
dc.date.issued | 2007-12-01 | en_US |
dc.identifier.other | 2-s2.0-49949105792 | en_US |
dc.identifier.other | 10.1109/IECON.2007.4460136 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=49949105792&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/61053 | - |
dc.description.abstract | In this paper, we propose a Thai phoneme recognition system with a soft phoneme segmentation. The soft phoneme segmentation technique is based on the characteristics of Thai language in that the vowel is the core of a syllable. The recognition system utilizes the discrete hidden Markov model to recognize the Thai phonemes, i.e., 21-class initial consonants, 18-class vowels, and 9-class final consonants. We use the Mel frequency with perceptual linear prediction as the features of a phoneme. We experiment the recognition system on both speaker-dependent and speaker-independent data sets recorded from 30 speakers. The experimental results show promising recognition performances in both cases. ©2007 IEEE. | en_US |
dc.subject | Engineering | en_US |
dc.title | Thai phoneme soft segmentation and recognition using hidden Markov models | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | IECON Proceedings (Industrial Electronics Conference) | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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