Abstract
Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. We participated in the NEWS 2018 shared task for the English-Vietnamese transliteration task.- Anthology ID:
- W18-2414
- Volume:
- Proceedings of the Seventh Named Entities Workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Nancy Chen, Rafael E. Banchs, Xiangyu Duan, Min Zhang, Haizhou Li
- Venue:
- NEWS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 95–100
- Language:
- URL:
- https://aclanthology.org/W18-2414
- DOI:
- 10.18653/v1/W18-2414
- Cite (ACL):
- Ngoc Tan Le and Fatiha Sadat. 2018. Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages. In Proceedings of the Seventh Named Entities Workshop, pages 95–100, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages (Le & Sadat, NEWS 2018)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/W18-2414.pdf