Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition

Lukuang Dong; Ziwei Li; Saierdaer Yusuyin; Xianyu Zhao; Zhijian Ou

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arXiv · 2603.29217

Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition

Lukuang Dong·Ziwei Li·Saierdaer Yusuyin·Xianyu Zhao·Zhijian Ou

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Abstract

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%.

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Lukuang Dong, Ziwei Li, Saierdaer Yusuyin, Xianyu Zhao, Zhijian Ou. 2026-09-05. Advancing LLM-based phoneme-to-grapheme for multilingual speech recognition. https://arxiv.org/abs/2603.29217

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