Benchmarking Large Language Models for Grapheme-to-Phoneme Conversion D
AI benchmark · Speech
Full listing name: Benchmarking Large Language Models for Grapheme-to-Phoneme Conversion: A Japanese Case Study
What is Benchmarking Large Language Models for Grapheme-to-Phoneme Conversion?
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.
Tier D
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- 37 models scored
- no proper name (a paper title or description)
- in curated RL directories (1 pt)
How we got to tier D
- Adoption: 37 models scored; no proper name (a paper title or description). Tier D.
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- In curated RL directories: 1 point, a floor of none.
- Not yet confirmed by a line from its own site or paper.
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Next: browse all benchmarks. Entry from the RL Research daily scrape of public sources, 2026-10-11 snapshot.