Grapheme to Phoneme Conversion for Standard Malay
This paper presents the use of Joint Source-Channel model (JSC) to carry out grapheme-to-phonetic (G2P) transcription process on Standard Malay (SM) [1]. Previous work on using the JSC for English to Chinese name transliteration indicates good results. Hence it is assumed that similar result can be...
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sg-smu-ink.lkcsb_research_smu-10242018-07-10T06:07:10Z Grapheme to Phoneme Conversion for Standard Malay Teoh, Boon Seong Tan, Yeow Kee Li, Haizhou This paper presents the use of Joint Source-Channel model (JSC) to carry out grapheme-to-phonetic (G2P) transcription process on Standard Malay (SM) [1]. Previous work on using the JSC for English to Chinese name transliteration indicates good results. Hence it is assumed that similar result can be achieved for the task of transforming SM Grapheme to SM Phoneme, especially out-of-vocabulary (OOV) SM words. This paper will discuss the SM language and the rules for text preprocessing, which are defined by [2] for SM language. A cross validation experiment was carried out and the result shows that the proposed JSC achieves an accuracy of 86.3% for the first best choice in close test and 85.7% in open test. 2004-11-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research_smu/25 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1024&context=lkcsb_research_smu http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business (SMU Access Only) eng Institutional Knowledge at Singapore Management University Technology and Innovation |
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Technology and Innovation Teoh, Boon Seong Tan, Yeow Kee Li, Haizhou Grapheme to Phoneme Conversion for Standard Malay |
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This paper presents the use of Joint Source-Channel model (JSC) to carry out grapheme-to-phonetic (G2P) transcription process on Standard Malay (SM) [1]. Previous work on using the JSC for English to Chinese name transliteration indicates good results. Hence it is assumed that similar result can be achieved for the task of transforming SM Grapheme to SM Phoneme, especially out-of-vocabulary (OOV) SM words. This paper will discuss the SM language and the rules for text preprocessing, which are defined by [2] for SM language. A cross validation experiment was carried out and the result shows that the proposed JSC achieves an accuracy of 86.3% for the first best choice in close test and 85.7% in open test. |
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text |
author |
Teoh, Boon Seong Tan, Yeow Kee Li, Haizhou |
author_facet |
Teoh, Boon Seong Tan, Yeow Kee Li, Haizhou |
author_sort |
Teoh, Boon Seong |
title |
Grapheme to Phoneme Conversion for Standard Malay |
title_short |
Grapheme to Phoneme Conversion for Standard Malay |
title_full |
Grapheme to Phoneme Conversion for Standard Malay |
title_fullStr |
Grapheme to Phoneme Conversion for Standard Malay |
title_full_unstemmed |
Grapheme to Phoneme Conversion for Standard Malay |
title_sort |
grapheme to phoneme conversion for standard malay |
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Institutional Knowledge at Singapore Management University |
publishDate |
2004 |
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https://ink.library.smu.edu.sg/lkcsb_research_smu/25 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1024&context=lkcsb_research_smu |
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