Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models
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.
682c18f7e404ba9ebb4d7790
Mahta Fetrat Qharabagh
claimed_verified
2025-05-21T09:03:32.878Z
657c45f6429a20edb520d7a8
https://cdn-avatars.huggingface.co/v1/production/uploads/657c45f6429a20edb520d7a8/jXVRbbaUzodZLupJiub84.png
Mahta Fetrat
user
MahtaFetrat
682c18f7e404ba9ebb4d7791
Zahra Dehghanian
682c18f7e404ba9ebb4d7792
Hamid R. Rabiee
Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models
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.
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%.
grapheme-to-phoneme homograph disambiguation semi-automated pipeline HomoRich dataset deep learning Persian rule-based methods HomoFast eSpeak