用两步法解决法语发音学习数据少的难题
A Two-Step Approach for Data-Efficient French Pronunciation Learning
- 分两步完成音素转换与词后处理，降低对标注数据依赖
- 仅用少量句子级发音数据就实现有效建模
- 适合资源有限环境下法语语音学习研究者使用
近期研究在处理法语复杂音韵现象时，依赖大量语言学知识或句级发音数据，但这些资源的构建成本高且困难。为此，本文提出一种新颖的两步法，包括音位转换和词后处理两个任务，并在仅有少量句级发音数据的条件下验证其有效性。结果表明，该方法能有效缓解大规模标注数据不足的问题，在资源受限环境下仍可实现对法语音韵现象的可靠建模，为低资源法语发音学习提供可行方案。
原文摘要 · Abstract (English)
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.
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