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

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.

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