Jonghwan Kim


2024

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A Two-Step Approach for Data-Efficient French Pronunciation Learning
Hoyeon Lee | Hyeeun Jang | Jonghwan Kim | Jaemin Kim
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

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.