%0 Conference Proceedings %T Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary %A Kyaw Thu, Ye %A Pa Pa, Win %A Sagisaka, Yoshinori %A Iwahashi, Naoto %Y Wu, Dekai %Y Bhattacharyya, Pushpak %S Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016) %D 2016 %8 December %I The COLING 2016 Organizing Committee %C Osaka, Japan %F kyaw-thu-etal-2016-comparison %X Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech (TTS) systems. In this paper, we evaluate seven G2P conversion approaches: Adaptive Regularization of Weight Vectors (AROW) based structured learning (S-AROW), Conditional Random Field (CRF), Joint-sequence models (JSM), phrase-based statistical machine translation (PBSMT), Recurrent Neural Network (RNN), Support Vector Machine (SVM) based point-wise classification, Weighted Finite-state Transducers (WFST) on a manually tagged Myanmar phoneme dictionary. The G2P bootstrapping experimental results were measured with both automatic phoneme error rate (PER) calculation and also manual checking in terms of voiced/unvoiced, tones, consonant and vowel errors. The result shows that CRF, PBSMT and WFST approaches are the best performing methods for G2P conversion on Myanmar language. %U https://aclanthology.org/W16-3702 %P 11-22