Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models

TL;DR

This paper introduces a semi-automated dataset creation pipeline for homograph disambiguation in G2P conversion, and advocates for rule-based methods informed by rich datasets to achieve fast, accurate disambiguation suitable for real-time applications.

Contribution

It presents HomoRich dataset construction, enhances a deep learning G2P system with this data, and improves a rule-based system for real-time homograph disambiguation.

Findings

30% improvement in disambiguation accuracy

Effective dataset generation pipeline

Enhanced rule-based G2P system for real-time use

Abstract

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.