用大模型提升日语音素转换，效果优于传统工具。
Benchmarking Large Language Models for Grapheme-to-Phoneme Conversion: A Japanese Case Study
- 分步处理：先分词再转音，比直接预测更准
- 最佳模型错误率低于0.52%，传统工具为1.03%
- 适合语音合成研究者和日语文本处理开发者
音素转换对可控且鲁棒的语音合成至关重要。本文在3000句人工标注的日语句子上，对比超过30个大语言模型与传统形态分析器在日语字符到音节转换任务上的表现。采用两种提示策略：解析模式（先分词后基于规则转音）和直接模式（直接预测音节）。结果表明，模型规模、版本及日语专项训练是关键因素；最优模型达到音节字符错误率低于0.52%，优于最佳传统工具（1.03%）。大多数模型在解析模式下表现更优，因规则后处理减轻了模型对复杂发音规则的负担。此外，将大模型生成的音节输入音节输入式语音合成系统，其发音质量优于端到端语音合成。
原文摘要 · Abstract (English)
Grapheme-to-phoneme (G2P) conversion is essential for controllable and robust text-to-speech, and large language models (LLMs), with broad linguistic knowledge, offer a promising approach. We benchmarked over 30 LLMs on Japanese G2P, comparing them with conventional morphological analyzers on 3000 manually annotated sentences. We evaluated two prompting strategies: a parse mode, where the LLM performs morphological analysis followed by rule-based kana conversion, and a direct mode, where the LLM directly predicts kana readings. The results show that model size, version, and Japanese-specialized training are key factors, with the best LLMs achieving kana character error rate below 0.52\% vs. the best conventional tool (1.03\%). Parse mode outperforms direct mode for most models, as rule-based post-processing relieves the LLM of handling complex pronunciation rules. We also show that feeding LLM-predicted kana into a kana-input TTS yields better pronunciation than end-to-end TTS.
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