title: Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models
summary: Homograph disambiguation remains a significant challenge in
  grapheme-to-phoneme (G2P) conversion, especially for low-resource languages.
  This challenge is twofold: (1) creating balanced and comprehensive homograph
  datasets is labor-intensive and costly, and (2) specific disambiguation
  strategies introduce additional latency, making them unsuitable for real-time
  applications such as screen readers and other accessibility tools. In this
  paper, we address both issues. First, we propose a semi-automated pipeline for
  constructing homograph-focused datasets, introduce the HomoRich dataset
  generated through this pipeline, and demonstrate its effectiveness by applying
  it to enhance a state-of-the-art deep learning-based G2P system for Persian.
  Second, we advocate for a paradigm shift - utilizing rich offline datasets to
  inform the development of fast, rule-based methods suitable for
  latency-sensitive accessibility applications like screen readers. To this end,
  we improve one of the most well-known rule-based G2P systems, eSpeak, into a
  fast homograph-aware version, HomoFast eSpeak. Our results show an approximate
  30% improvement in homograph disambiguation accuracy for the deep
  learning-based and eSpeak systems.
paper_authors._id: 682c18f7e404ba9ebb4d7790
paper_authors.name: Mahta Fetrat Qharabagh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T09:03:32.878Z
paper_authors._id: 657c45f6429a20edb520d7a8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/657c45f6429a20edb520d7a8/jXVRbbaUzodZLupJiub84.png
paper_authors.fullname: Mahta Fetrat
paper_authors.type: user
paper_authors.user: MahtaFetrat
paper_authors._id: 682c18f7e404ba9ebb4d7791
paper_authors.name: Zahra Dehghanian
paper_authors._id: 682c18f7e404ba9ebb4d7792
paper_authors.name: Hamid R. Rabiee
paper_title: Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models
paper_summary: Homograph disambiguation remains a significant challenge in
  grapheme-to-phoneme (G2P) conversion, especially for low-resource languages.
  This challenge is twofold: (1) creating balanced and comprehensive homograph
  datasets is labor-intensive and costly, and (2) specific disambiguation
  strategies introduce additional latency, making them unsuitable for real-time
  applications such as screen readers and other accessibility tools. In this
  paper, we address both issues. First, we propose a semi-automated pipeline for
  constructing homograph-focused datasets, introduce the HomoRich dataset
  generated through this pipeline, and demonstrate its effectiveness by applying
  it to enhance a state-of-the-art deep learning-based G2P system for Persian.
  Second, we advocate for a paradigm shift - utilizing rich offline datasets to
  inform the development of fast, rule-based methods suitable for
  latency-sensitive accessibility applications like screen readers. To this end,
  we improve one of the most well-known rule-based G2P systems, eSpeak, into a
  fast homograph-aware version, HomoFast eSpeak. Our results show an approximate
  30% improvement in homograph disambiguation accuracy for the deep
  learning-based and eSpeak systems.
paper_ai_summary: A semi-automated pipeline and a fast rule-based system enhance homograph disambiguation in grapheme-to-phoneme conversion for low-resource languages, improving accuracy by 30%.
paper_ai_keywords: grapheme-to-phoneme homograph disambiguation semi-automated pipeline HomoRich dataset deep learning Persian rule-based methods HomoFast eSpeak