# BanglaTalk: Towards Real-Time Speech Assistance for Bengali Regional Dialects

arXiv: https://arxiv.org/abs/2510.06188 · HTML: https://mlsanity.com/paper/2510.06188/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [A Complete Survey on LLM-based AI Chatbots](https://mlsanity.com/paper/2406.16937/) (2024)
- [Towards Building ASR Systems for the Next Billion Users](https://mlsanity.com/paper/2111.03945/) (2021)
- [A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/1709.08243/) (2017)
- [High-Quality, Low-Delay Music Coding in the Opus Codec](https://mlsanity.com/paper/1602.04845/) (2016)
- [Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla](https://mlsanity.com/paper/2507.01931/) (2025)
- [Dynamic Speech Endpoint Detection with Regression Targets](https://mlsanity.com/paper/2210.14252/) (2022)
- [Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models](https://mlsanity.com/paper/2209.12650/) (2022)

## Built on by (most cited first, 3 of 3)

- [Many Dialects, Many Languages, One Cultural Lens: Evaluating Multilingual VLMs for Bengali Culture Understanding Across Historically Linked Languages and Regional Dialects](https://mlsanity.com/paper/2603.21165/) (2026)
- [BanglaIPA: Towards Robust Text-to-IPA Transcription with Contextual Rewriting in Bengali](https://mlsanity.com/paper/2601.01778/) (2026)
- [Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms](https://mlsanity.com/paper/2608.22335/) (2026)

## Abstract (arXiv)

Real-time speech assistants are becoming increasingly popular for ensuring improved accessibility to information. Bengali, being a low-resource language with a high regional dialectal diversity, has seen limited progress in developing such systems. Existing systems are not optimized for real-time use and focus only on standard Bengali. In this work, we present BanglaTalk, the first real-time speech assistance system for Bengali regional dialects. BanglaTalk follows the client-server architecture and uses the Real-time Transport Protocol (RTP) to ensure low-latency communication. To address dialectal variation, we introduce a dialect-aware ASR system, BRDialect, developed by fine-tuning the IndicWav2Vec model in ten Bengali regional dialects. It outperforms the baseline ASR models by 12.41-33.98% on the RegSpeech12 dataset. Furthermore, BanglaTalk can operate at a low bandwidth of 24 kbps while maintaining an average end-to-end delay of 4.9 seconds. Low bandwidth usage and minimal end-to-end delay make the system both cost-effective and interactive for real-time use cases, enabling inclusive and accessible speech technology for the diverse community of Bengali speakers. Code is available in https://github.com/Jak57/BanglaTalk