# BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization

arXiv: https://arxiv.org/abs/2411.10879 · HTML: https://mlsanity.com/paper/2411.10879/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla](https://mlsanity.com/paper/2205.11081/) (2022)
- [Sparks of Large Audio Models: A Survey and Outlook](https://mlsanity.com/paper/2308.12792/) (2023)
- [Vashantor: A Large-scale Multilingual Benchmark Dataset for Automated Translation of Bangla Regional Dialects to Bangla Language](https://mlsanity.com/paper/2311.11142/) (2023)
- [English to Bangla Machine Translation Using Recurrent Neural Network](https://mlsanity.com/paper/2106.07225/) (2021)
- [Bengali Common Voice Speech Dataset for Automatic Speech Recognition](https://mlsanity.com/paper/2206.14053/) (2022)
- [End-to-End Automatic Speech Recognition model for the Sudanese Dialect](https://mlsanity.com/paper/2212.10826/) (2022)
- [End to End Bangla Speech Synthesis](https://mlsanity.com/paper/2108.00500/) (2021)
- [Automatic Speech Recognition for Biomedical Data in Bengali Language](https://mlsanity.com/paper/2406.12931/) (2024)

## Built on by (most cited first, 2 of 2)

- [INDIC DIALECT: A Multi Task Benchmark to Evaluate and Translate in Indian Language Dialects](https://mlsanity.com/paper/2601.10388/) (2026)
- [Evaluating Japanese Dialect Robustness Across Speech and Text-based Large Language Models](https://mlsanity.com/paper/2606.25436/) (2026)

## Abstract (arXiv)

This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional languages, are distinctive variations of a language spoken in a particular location and are identified by their phonetics, pronunciations, and lexicon. Subtle changes in pronunciation and intonation are also influenced by geographic location, educational attainment, and socioeconomic status. Dialect standardization is needed to ensure effective communication, educational consistency, access to technology, economic opportunities, and the preservation of linguistic resources while respecting cultural diversity. Being the fifth most spoken language with around 55 distinct dialects spoken by 160 million people, addressing Bangla dialects is crucial for developing inclusive communication tools. However, limited research exists due to a lack of comprehensive datasets and the challenges of handling diverse dialects. With the advancement in multilingual Large Language Models (mLLMs), emerging possibilities have been created to address the challenges of dialectal Automated Speech Recognition (ASR) and Machine Translation (MT). This study presents an end-to-end pipeline for converting dialectal Noakhali speech to standard Bangla speech. This investigation includes constructing a large-scale diverse dataset with dialectal speech signals that tailored the fine-tuning process in ASR and LLM for transcribing the dialect speech to dialect text and translating the dialect text to standard Bangla text. Our experiments demonstrated that fine-tuning the Whisper ASR model achieved a CER of 0.8% and WER of 1.5%, while the BanglaT5 model attained a BLEU score of 41.6% for dialect-to-standard text translation.