Bengali Common Voice Speech Dataset for Automatic Speech Recognition
Not on the map. Why?
Abstract (arXiv)
Bengali is one of the most spoken languages in the world with over 300 million speakers globally. Despite its popularity, research into the development of Bengali speech recognition systems is hindered due to the lack of diverse open-source datasets. As a way forward, we have crowdsourced the Bengali Common Voice Speech Dataset, which is a sentence-level automatic speech recognition corpus. Collected on the Mozilla Common Voice platform, the dataset is part of an ongoing campaign that has led to the collection of over 400 hours of data in 2 months and is growing rapidly. Our analysis shows that this dataset has more speaker, phoneme, and environmental diversity compared to the OpenSLR Bengali ASR dataset, the largest existing open-source Bengali speech dataset. We present insights obtained from the dataset and discuss key linguistic challenges that need to be addressed in future versions. Additionally, we report the current performance of a few Automatic Speech Recognition (ASR) algorithms and set a benchmark for future research.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 3 of its 44 references that are in the map. First posted on arXiv 28 Jun 2022.
Builds on
3 of 44 references.
References · 3
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2020A Large Multi-Target Dataset of Common Bengali Handwritten Graphemescited by 12
- 2020Synthetic Error Dataset Generation Mimicking Bengali Writing Patterncited by 4
Built on by
Citing papers per year: 2022: 4; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2022Applying wav2vec2 for Speech Recognition on Bengali Common Voices Datasetcited by 4
- 2022Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Modelscited by 3
- 2022An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learningcited by 2
- 2024BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardizationcited by 2
- 2022Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Banglacited by 0
- 2026A Holistic Framework for Robust Bangla ASR and Speaker Diarization with Optimized VAD and CTC Alignmentcited by 0
- 2026A Holistic Framework for Robust Bangla ASR and Speaker Diarization with Optimized VAD and CTC Alignment26 Feb 2026
- 2024BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization16 Nov 2024
- 2022Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla24 Oct 2022
- 2022An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning16 Sep 2022
- 2022Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models13 Sep 2022
- 2022Applying wav2vec2 for Speech Recognition on Bengali Common Voices Dataset11 Sep 2022