BanglaTalk: Towards Real-Time Speech Assistance for Bengali Regional Dialects
Not on the map. Why?
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
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 54 references that are in the map. First posted on arXiv 7 Oct 2025.
Builds on
10 of 54 references.
References · 10
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
- 2023Scaling Speech Technology to 1,000+ Languagescited by 401
- 2024A Complete Survey on LLM-based AI Chatbotscited by 71
- 2021Towards Building ASR Systems for the Next Billion Userscited by 45
- 2017A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancementcited by 34
- 2016High-Quality, Low-Delay Music Coding in the Opus Codeccited by 12
- 2025Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Banglacited by 5
- 2022Dynamic Speech Endpoint Detection with Regression Targetscited by 4
- 2022Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Modelscited by 3
Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026Many Dialects, Many Languages, One Cultural Lens: Evaluating Multilingual VLMs for Bengali Culture Understanding Across Historically Linked Languages and Regional Dialectscited by 3
- 2026BanglaIPA: Towards Robust Text-to-IPA Transcription with Contextual Rewriting in Bengalicited by 2
- 2026Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harmscited by 1
- 2026Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms23 Aug 2026
- 2026Many Dialects, Many Languages, One Cultural Lens: Evaluating Multilingual VLMs for Bengali Culture Understanding Across Historically Linked Languages and Regional Dialects22 Mar 2026
- 2026BanglaIPA: Towards Robust Text-to-IPA Transcription with Contextual Rewriting in Bengali5 Jan 2026