A Two-Step Approach for Data-Efficient French Pronunciation Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909342185291776 |
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| author | Lee, Hoyeon Jang, Hyeeun Kim, Jong-Hwan Kim, Jae-Min |
| author_facet | Lee, Hoyeon Jang, Hyeeun Kim, Jong-Hwan Kim, Jae-Min |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Two-Step Approach for Data-Efficient French Pronunciation Learning Lee, Hoyeon Jang, Hyeeun Kim, Jong-Hwan Kim, Jae-Min Computation and Language Artificial Intelligence 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. |
| title | A Two-Step Approach for Data-Efficient French Pronunciation Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.05698 |