@InProceedings{kyawthu-EtAl:2016:WSSANLP2016,
  author    = {Kyaw Thu, Ye  and  Pa Pa, Win  and  Sagisaka, Yoshinori  and  Iwahashi, Naoto},
  title     = {Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary},
  booktitle = {Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {11--22},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/W16-3702}
}