Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908924234432512 |
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| author | Dong, Lukuang Li, Ziwei Yusuyin, Saierdaer Zhao, Xianyu Ou, Zhijian |
| author_facet | Dong, Lukuang Li, Ziwei Yusuyin, Saierdaer Zhao, Xianyu Ou, Zhijian |
| contents | Phoneme-based ASR factorizes recognition into speech-to-phoneme (S2P) and phoneme-to-grapheme (P2G), enabling cross-lingual acoustic sharing while keeping language-specific orthography in a separate module. While large language models (LLMs) are promising for P2G, multilingual P2G remains challenging due to language-aware generation and severe cross-language data imbalance. We study multilingual LLM-based P2G on the ten-language CV-Lang10 benchmark. We examine robustness strategies that account for S2P uncertainty, including DANP and Simplified SKM (S-SKM). S-SKM is a Monte Carlo approximation that avoids CTC-based S2P probability weighting in P2G training. Robust training and low-resource oversampling reduce the average WER from 10.56% to 7.66%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29217 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition Dong, Lukuang Li, Ziwei Yusuyin, Saierdaer Zhao, Xianyu Ou, Zhijian Audio and Speech Processing Computation and Language Sound Phoneme-based ASR factorizes recognition into speech-to-phoneme (S2P) and phoneme-to-grapheme (P2G), enabling cross-lingual acoustic sharing while keeping language-specific orthography in a separate module. While large language models (LLMs) are promising for P2G, multilingual P2G remains challenging due to language-aware generation and severe cross-language data imbalance. We study multilingual LLM-based P2G on the ten-language CV-Lang10 benchmark. We examine robustness strategies that account for S2P uncertainty, including DANP and Simplified SKM (S-SKM). S-SKM is a Monte Carlo approximation that avoids CTC-based S2P probability weighting in P2G training. Robust training and low-resource oversampling reduce the average WER from 10.56% to 7.66%. |
| title | Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition |
| topic | Audio and Speech Processing Computation and Language Sound |
| url | https://arxiv.org/abs/2603.29217 |