A Two-Step Approach for Data-Efficient French Pronunciation Learning
Hoyeon Lee, Hyeeun Jang, Jonghwan Kim, Jaemin Kim
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.
BibTeX
@inproceedings{lee-etal-2024-two,
title = "A Two-Step Approach for Data-Efficient {F}rench Pronunciation Learning",
author = "Lee, Hoyeon and
Jang, Hyeeun and
Kim, Jonghwan and
Kim, Jaemin",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1064/",
doi = "10.18653/v1/2024.emnlp-main.1064",
pages = "19096--19103"
}