A Two-Step Approach for Data-Efficient French Pronunciation Learning
<|reference_start|>A Two-Step Approach for Data-Efficient French Pronunciation Learning: 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.<|reference_end|>
@article{lee2024a,
    title={A Two-Step Approach for Data-Efficient French Pronunciation Learning},
    author={Hoyeon Lee, Hyeeun Jang, Jong-Hwan Kim, Jae-Min Kim},
    journal={arXiv preprint arXiv:2410.05698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05698},
    primaryClass={cs.CL cs.AI}
}