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LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study

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arxiv 2409.08554 v1 pith:JXNCZBEN submitted 2024-09-13 cs.CL

Mahta Fetrat Qharabagh , Zahra Dehghanian , Hamid R. Rabiee This is my paper · ORCID
classification cs.CL
keywords languageconversionllmsgrapheme-to-phonemelikemethodsperformancepersian
verification ladder T0 review T1 audit T2 compute T3 formal
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Grapheme-to-phoneme (G2P) conversion is critical in speech processing, particularly for applications like speech synthesis. G2P systems must possess linguistic understanding and contextual awareness of languages with polyphone words and context-dependent phonemes. Large language models (LLMs) have recently demonstrated significant potential in various language tasks, suggesting that their phonetic knowledge could be leveraged for G2P. In this paper, we evaluate the performance of LLMs in G2P conversion and introduce prompting and post-processing methods that enhance LLM outputs without additional training or labeled data. We also present a benchmarking dataset designed to assess G2P performance on sentence-level phonetic challenges of the Persian language. Our results show that by applying the proposed methods, LLMs can outperform traditional G2P tools, even in an underrepresented language like Persian, highlighting the potential of developing LLM-aided G2P systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context

    cs.SD 2025-05 conditional novelty 6.0 of 10

    An LLM-based ASR error corrector trained on synthetic rare-word speech and given simplified phonetic context lowers WER/CER and raises rare-word recall on English and Japanese benchmarks.

  2. P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs

    cs.CL 2025-07 reject novelty 5.0 of 10

    P-CoT prompting improves many LLM results on PhonologyBench tasks, but it does not consistently beat baselines across all models and tasks as the paper claims.

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