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        <title>Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models</title>
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        <namePart type="given">Tomohiro</namePart>
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            <title>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
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        <name type="personal">
            <namePart type="given">Marine</namePart>
            <namePart type="family">Carpuat</namePart>
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        <name type="personal">
            <namePart type="given">Marie-Catherine</namePart>
            <namePart type="family">de Marneffe</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="given">Vladimir</namePart>
            <namePart type="family">Meza Ruiz</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.naacl-main.315</identifier>
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        <date>2022-07</date>
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%0 Conference Proceedings
%T Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models
%A Yamasaki, Tomohiro
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F yamasaki-2022-grapheme
%X 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.
%R 10.18653/v1/2022.naacl-main.315
%U https://aclanthology.org/2022.naacl-main.315
%U https://doi.org/10.18653/v1/2022.naacl-main.315
%P 4251-4255

Markdown (Informal)

[Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models](https://aclanthology.org/2022.naacl-main.315) (Yamasaki, NAACL 2022)

• Grapheme-to-Phoneme Conversion for Thai using Neural Regression Models (Yamasaki, NAACL 2022)

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.