Abstract
Grapheme-to-Phoneme (G2P) conversion is a necessary step for speech synthesis and speech recognition. In this paper, we attempt to apply a Statistical Machine Translation (SMT) approach for Myanmar G2P conversion. The performance of G2P conversion with SMT is measured in terms of BLEU score, syllable phoneme accuracy and processing time. The experimental results show that G2P conversion with SMT is outperformed a Conditional Random Field (CRF) approach. Moreover, the training time was considerably faster than the CRF approach.
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Acknowledgment
We thank Ms. Aye Mya Hlaing (UCSY, Yangon, Myanmar) and Ms. Hay Mar Soe Naing (UCSY, Yangon, Myanmar) for their help in phoneme tagging and checking for MLC dictionary and selected 5,276 sentences.
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Thu, Y.K., Pa, W.P., Finch, A., Ni, J., Sumita, E., Hori, C. (2016). The Application of Phrase Based Statistical Machine Translation Techniques to Myanmar Grapheme to Phoneme Conversion. In: Hasida, K., Purwarianti, A. (eds) Computational Linguistics. PACLING 2015. Communications in Computer and Information Science, vol 593. Springer, Singapore. https://doi.org/10.1007/978-981-10-0515-2_17
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DOI: https://doi.org/10.1007/978-981-10-0515-2_17
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