Fast, Not Fancy: Rethinking G 2 P with Rich Data and Statistical Models (2025)

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, statistical 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 percentage-point improvement in homograph disambiguation accuracy for the deep learning-based and eSpeak systems.

## introduction grapheme-to-phoneme (g2p) conversion is a crucial step in many fast text-to-speech (tts) models @xcite . it refers to the task of converting a given written text into its corresponding sequence of phonemes-how it is pronounced. there are several formats for representing phoneme sequences, one of the most widely used being the international phonetic alphabet (ipa) @xcite . as an example, the phoneme sequence for the sentence "i will read it" is /ai wil ri:d it/ in ipa format. the complexity of g2p conversion varies by language. some languages like turkish and spanish are highly phonetic, meaning a near one-to-one correspondence between spelling and pronunciation @xcite . in contrast, in many other languages, such as persian, g2p is more complex due to exceptions and rules that depend on context @xcite . one such challenge is handling homographs-words spelled the same but pronounced differently depending on context. for example, the word "read" is pronounced /red/ in the past tense ("i read this book yesterday") and /ri:d/ in the present tense ("i read the book every night"). unfortunately, sentence-level g2p datasets are extremely scarce in low-resource languages. this scarcity stems from the fact that phonemization is a time-consuming and costly process that requires expert annotators. homograph-specific datasets are even rarer, as they depend on source corpora that must meet strict conditions: they should contain a wide range of homographs and provide a balanced number of examples for each pronunciation. without this balance, the resulting models will fail to learn some homographs and tend to default to the more frequent pronunciation in ambiguous cases. beyond data scarcity, there is also a methodological challenge in g2p conversion. the two primary approaches are neural models @xcite @xcite and nonneural methods @xcite @xcite . while neural methods have gained popularity due to their flexibility and learning capacity, they often suffer from high inference latency, making them unsuitable for real-time applications such as screen readers that serve accessibility needs. this motivates a renewed focus on non-neural approaches, aiming to improve their accuracy while preserving their inherent speed. this work proposes a practical approach for generating a rich and balanced homograph dataset. we demonstrate that such a dataset not only boosts the homograph disambiguation accuracy of neural g2p models but also significantly enhances the performance of rule-based systems. specifically, we show that by incorporating a simple, fast statistical method that leverages the proposed dataset, rule-based models can be equipped with context understanding, leading to improved handling of homographs without sacrificing speed. our key contributions are as follows: ## related works in this section, we review homograph disambiguation from two perspectives: the common methods and the datasets developed to address challenges in low-resource settings like persian. ## methodology developing an effective g2p model requires both high-quality data and the tools to make use of it. this section outlines our data generation process and how we leveraged it to improve g2p models. ## conclusion in this work, we tackled two persistent challenges in homograph disambiguation for low-resource languages: the high cost of dataset creation and the latency constraints of real-time g2p applications. we proposed a semi-automated pipeline for building homograph-rich datasets and introduced ho-morich, the first large-scale, openly licensed persian homograph dataset. using this resource, we achieved a 29.72 percentage-point improvement in homograph accuracy for a state-of-the-art neural g2p model. to bridge the gap between accuracy and real-time performance, we further developed a lightweight, context-aware statistical method that enhances homograph handling with minimal computational overhead. integrated into the widely used espeak engine, this method led to homo-fast espeak, a fast, homograph-aware g2p system that improves disambiguation accuracy by 30.66 percentage-points while retaining the responsiveness crucial for screen readers and other accessibility tools. our results highlight the potential of using highquality offline datasets not only to train neural models, but also to enrich and modernize traditional rule-based systems. by releasing all resources under a cc0-1.0 license, we aim to foster further research and practical adoption in accessibility technologies for low-resource languages.