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.- Anthology ID:
- W16-3702
- Volume:
- Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- WSSANLP
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 11–22
- Language:
- URL:
- https://aclanthology.org/W16-3702
- DOI:
- Cite (ACL):
- Ye Kyaw Thu, Win Pa Pa, Yoshinori Sagisaka, and Naoto Iwahashi. 2016. Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary. In Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016), pages 11–22, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary (Kyaw Thu et al., WSSANLP 2016)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W16-3702.pdf