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.
@article{arxiv.2206.14053,
title = {Bengali Common Voice Speech Dataset for Automatic Speech Recognition},
author = {Samiul Alam and Asif Sushmit and Zaowad Abdullah and Shahrin Nakkhatra and MD. Nazmuddoha Ansary and Syed Mobassir Hossen and Sazia Morshed Mehnaz and Tahsin Reasat and Ahmed Imtiaz Humayun},
journal= {arXiv preprint arXiv:2206.14053},
year = {2022}
}