title: LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study
summary: 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.
paper_authors._id: 66e9c55095c7a0c91b804500
paper_authors.name: Mahta Fetrat Qharabagh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-18T07:48:51.629Z
paper_authors._id: 657c45f6429a20edb520d7a8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/657c45f6429a20edb520d7a8/4vn_y4o22StBBUIw_W3cH.jpeg
paper_authors.fullname: Mahta Fetrat
paper_authors.type: user
paper_authors.user: MahtaFetrat
paper_authors._id: 66e9c55095c7a0c91b804501
paper_authors.name: Zahra Dehghanian
paper_authors._id: 66e9c55095c7a0c91b804502
paper_authors.name: Hamid R. Rabiee
paper_title: LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study
paper_summary: 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.
paper_ai_keywords: grapheme-to-phoneme conversion speech synthesis linguistic understanding contextual awareness polyphone words context-dependent phonemes large language models prompting post-processing methods benchmarking dataset sentence-level phonetic challenges Persian language traditional G2P tools LLM-aided G2P systems