BanglaIPA: Towards Robust Text-to-IPA Transcription with Contextual Rewriting in Bengali

TL;DR

BanglaIPA is a new system for Bengali IPA transcription that effectively handles dialectal variations, numerals, and unseen words, significantly improving accuracy and efficiency over previous methods.

Contribution

It introduces a novel integration of character-based vocabulary with word-level alignment and a precomputed dictionary, enhancing robustness and generalization in Bengali IPA transcription.

Findings

Outperforms baseline models by 58.4-78.7%

Achieves 11.4% mean word error rate

Handles regional dialects and numerals effectively

Abstract

Despite its widespread use, Bengali lacks a robust automated International Phonetic Alphabet (IPA) transcription system that effectively supports both standard language and regional dialectal texts. Existing approaches struggle to handle regional variations, numerical expressions, and generalize poorly to previously unseen words. To address these limitations, we propose BanglaIPA, a novel IPA generation system that integrates a character-based vocabulary with word-level alignment. The proposed system accurately handles Bengali numerals and demonstrates strong performance across regional dialects. BanglaIPA improves inference efficiency by leveraging a precomputed word-to-IPA mapping dictionary for previously observed words. The system is evaluated on the standard Bengali and six regional variations of the DUAL-IPA dataset. Experimental results show that BanglaIPA outperforms baseline IPA transcription models by 58.4-78.7% and achieves an overall mean word error rate of 11.4%, highlighting its robustness in phonetic transcription generation for the Bengali language.