Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models

Tomohiro Yamasaki


Abstract
We propose a novel Thai grapheme-to-phoneme conversion method based on a neural regression model that is trained using neural networks to predict the similarity between a candidate and the correct pronunciation. After generating a set of candidates for an input word or phrase using the orthography rules, this model selects the best-similarity pronunciation from the candidates. This method can be applied to languages other than Thai simply by preparing enough orthography rules, and can reduce the mistakes that neural network models often make. We show that the accuracy of the proposed method is .931, which is comparable to that of encoder-decoder sequence models. We also demonstrate that the proposed method is superior in terms of the difference between correct and predicted pronunciations because incorrect, strange output sometimes occurs when using encoder-decoder sequence models but the error is within the expected range when using the proposed method.
Anthology ID:
2022.naacl-main.315
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4251–4255
Language:
URL:
https://aclanthology.org/2022.naacl-main.315
DOI:
10.18653/v1/2022.naacl-main.315
Bibkey:
Cite (ACL):
Tomohiro Yamasaki. 2022. Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4251–4255, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models (Yamasaki, NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.315.pdf
Software:
 2022.naacl-main.315.software.zip