Thai soundex using Thai phonetic distance algorithm
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2014
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2557
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eng
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65 leaves
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b192203
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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National Institute of Development Administration. Library and Information Center
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Chonlasith Jucksriporn (2014). Thai soundex using Thai phonetic distance algorithm. Retrieved from: http://repository.nida.ac.th/handle/662723737/4125.
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Thai soundex using Thai phonetic distance algorithm
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Abstract
Homophones are words with similar sound. Searching for homophones is not only looking for the words with similar spelling, but it should also be looking for the words with similar pronunciation. Specifically in Thai, some consonant clusters can be pronounced in different ways, such as // can be pronounced as // or // depending on cach particular word. This makes Thoi word pronunciation and dictation more dilliculi Thai sounder could not handle these consonant clusters properly: for example, many Thai soundex methods showed cncoded results of a consonant cluster /n/ in the name w as with the letter which does not represent the correct initial consonant sound, lol.Homophones are words with similar sound. Searching for homophones is not only looking for the words with similar spelling, but it should also be looking for the words with similar pronunciation. Specifically in Thai, some consonant clusters can be pronounced in different ways, such as // can be pronounced as // or // depending on cach particular word. This makes Thoi word pronunciation and dictation more dilliculi Thai sounder could not handle these consonant clusters properly: for example, many Thai soundex methods showed cncoded results of a consonant cluster /n/ in the name w as with the letter which does not represent the correct initial consonant sound, lol.
This research proposes a technique to find homophones by calculating the distance between words using their phonetics, instead of spelling. The research also proposes an approach to syllabify word using Thai Minimum Cluster based on trigram statistical model and an improved phonetic representation to resolve ambiguities in Thai standard transcription
The experimental evaluations were performed on a name corpus of Thai people and places. The results show that the proposed method achieves average precision and average recall of 99.8.3%
This research proposes a technique to find homophones by calculating the distance between words using their phonetics, instead of spelling. The research also proposes an approach to syllabify word using Thai Minimum Cluster based on trigram statistical model and an improved phonetic representation to resolve ambiguities in Thai standard transcription
The experimental evaluations were performed on a name corpus of Thai people and places. The results show that the proposed method achieves average precision and average recall of 99.8.3%
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Dissertations (Ph.D. (Computer Science))National Institute of Development Administration, 2014