Multi-Module G2P Converter for Persian Focusing on Relations between Words
This improves G2P conversion for Persian language processing, though it is incremental as it builds on existing methods.
The paper tackled grapheme-to-phoneme (G2P) conversion for Persian by comparing end-to-end and multi-module frameworks, achieving a 94.48% word-level accuracy that outperforms previous systems.
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.