# Multi-Module G2P Converter for Persian Focusing on Relations between Words

arXiv: https://arxiv.org/abs/2208.01371 · HTML: https://mlsanity.com/paper/2208.01371/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Transformer based Grapheme-to-Phoneme Conversion](https://mlsanity.com/paper/2004.06338/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models](https://mlsanity.com/paper/2505.12973/) (2025)

## Abstract (arXiv)

In this paper, we investigate the application of end-to-end and multi-module frameworks for G2P conversion for the Persian language. The results demonstrate that our proposed multi-module G2P system outperforms our end-to-end systems in terms of accuracy and speed. The system consists of a pronunciation dictionary as our look-up table, along with separate models to handle homographs, OOVs and ezafe in Persian created using GRU and Transformer architectures. The system is sequence-level rather than word-level, which allows it to effectively capture the unwritten relations between words (cross-word information) necessary for homograph disambiguation and ezafe recognition without the need for any pre-processing. After evaluation, our system achieved a 94.48% word-level accuracy, outperforming the previous G2P systems for Persian.