Abstract
Recent studies have addressed intricate phonological phenomena in French, relying on either extensive linguistic knowledge or a significant amount of sentence-level pronunciation data. However, creating such resources is expensive and non-trivial. To this end, we propose a novel two-step approach that encompasses two pronunciation tasks: grapheme-to-phoneme and post-lexical processing. We then investigate the efficacy of the proposed approach with a notably limited amount of sentence-level pronunciation data. Our findings demonstrate that the proposed two-step approach effectively mitigates the lack of extensive labeled data, and serves as a feasible solution for addressing French phonological phenomena even under resource-constrained environments.- Anthology ID:
- 2024.emnlp-main.1064
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 19096–19103
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.1064/
- DOI:
- 10.18653/v1/2024.emnlp-main.1064
- Cite (ACL):
- Hoyeon Lee, Hyeeun Jang, Jonghwan Kim, and Jaemin Kim. 2024. A Two-Step Approach for Data-Efficient French Pronunciation Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19096–19103, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- A Two-Step Approach for Data-Efficient French Pronunciation Learning (Lee et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.1064.pdf