## Goal of the Competition The goal of this competition is to recognize Bengali speech from out-of-distribution audio recordings. You will build a model trained on the first Massively Crowdsourced (MaCro) Bengali speech dataset with 1,200 hours of data from ~24,000 people from India and Bangladesh. The test set contains samples from 17 different domains that are not present in training. Your efforts could improve Bengali speech recognition using the first Bengali out-of-distribution speech recognition dataset. In addition, your submission will be among the first open-source speech recognition methods for Bengali. ## Context Bengali is one of the most spoken languages in the world, with approximately 340 million native and second-language speakers globally. With that comes diversity in dialects and prosodic features (combinations of sounds). For example, Muslim religious sermons in Bengali are often delivered with a pace and tonality that is significantly different from regular speech. Such ‘shifts’ can be challenging even for commercially available speech recognition methods (the Google Speech API for Bengali has a Word Error Rate of 74% for Bengali religious sermons). There are no robust open-source speech recognition models for Bengali currently, though your data science skills could certainly help change that. In particular, out-of-distribution generalization is a common machine learning problem. When test and training data are similar, they’re in-distribution. To account for Bengali’s diversity, this competition’s data is intentionally out-of-distribution, with the challenge to improve results.. Competition host Bengali.AI is a non-profit community initiative working to accelerate language technology research for Bengali (known locally as Bangla). Bengali.AI crowdsources large-scale datasets through community-driven collection campaigns and crowdsource solutions for their datasets through research competitions. All the outcomes from Bengali.AI's two-pronged approach, including datasets and trained models, are open-sourced for public use. Your work in this competition could have an impact beyond speech recognition improvements for one of the world's most popular, yet low-resource languages. You could also provide a much-needed push towards solving one of speech recognition's major challenges, out-of-distribution generalization. ## Acknowledgments We specially thank our collaborators from Aspire to Innovate (a2i) program by the Govt. Bangladesh, Bangladesh University of Engineering and Technology (BUET), and Shahjalal University of Science and Technology (SUST). > **This is a Code Competition. Refer to [Code Requirements](https://kaggle.com/competitions/bengaliai-speech/overview/code-requirements) for details.**