# Benchmarking Large Language Models for Grapheme-to-Phoneme Conversion: A Japanese Case Study

arXiv: https://arxiv.org/abs/2606.22009 · HTML: https://mlsanity.com/paper/2606.22009/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching](https://mlsanity.com/paper/2410.06885/) (2024)
- [CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://mlsanity.com/paper/2412.10117/) (2024)
- [Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities](https://mlsanity.com/paper/2404.17790/) (2024)
- [Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/1506.00196/) (2015)
- [LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study](https://mlsanity.com/paper/2409.08554/) (2024)
- [PhonologyBench: Evaluating Phonological Skills of Large Language Models](https://mlsanity.com/paper/2404.02456/) (2024)
- [Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge Retrieval with Large Language Models](https://mlsanity.com/paper/2411.07563/) (2024)
- [CC-G2PnP: Streaming Grapheme-to-Phoneme and prosody with Conformer-CTC for unsegmented languages](https://mlsanity.com/paper/2602.17157/) (2026)
- [Vaporetto: Efficient Japanese Tokenization Based on Improved Pointwise Linear Classification](https://mlsanity.com/paper/2406.17185/) (2024)
- [A Comparative Analysis of Word Segmentation, Part-of-Speech Tagging, and Named Entity Recognition for Historical Chinese Sources, 1900-1950](https://mlsanity.com/paper/2503.19844/) (2025)

## Built on by (most cited first, 3 of 3)

- [Dictionary-Constrained Grapheme-to-Phoneme for Unsegmented Languages from LLM-Annotated Data](https://mlsanity.com/paper/2609.19805/) (2026)
- [Ruby-ASR: Evidence-Preserving Supervision for Joint Orthographic and Lexical-Reading Recognition](https://mlsanity.com/paper/2609.27289/) (2026)
- [Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring](https://mlsanity.com/paper/2609.30924/) (2026)

## Abstract (arXiv)

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.