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In this paper, we investigate the application of end-to-end and multi-moduleframeworks for G2P conversion for the Persian language. The results demonstratethat our proposed multi-module G2P system outperforms our end-to-end systems interms of accuracy and speed. The system consists of a pronunciation dictionaryas our look-up table, along with separate models to handle homographs, OOVs andezafe in Persian created using GRU and Transformer architectures. The system issequence-level rather than word-level, which allows it to effectively capturethe unwritten relations between words (cross-word information) necessary forhomograph disambiguation and ezafe recognition without the need for anypre-processing. After evaluation, our system achieved a 94.48% word-levelaccuracy, outperforming the previous G2P systems for Persian.

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