OOD-Speech: A Large Bengali Speech Recognition Dataset for
  Out-of-Distribution Benchmarking
Fazle Rabbi Rakib Souhardya Saha Dip Samiul Alam Nazia Tasnim Md. Istiak Hossain Shihab Md. Nazmuddoha Ansary Syed Mobassir Hossen Marsia Haque Meghla Mamunur Mamun Farig Sadeque Sayma Sultana Chowdhury Tahsin Reasat Asif Sushmit Ahmed Imtiaz Humayun
We present OOD-Speech, the first out-of-distribution (OOD) benchmarking
dataset for Bengali automatic speech recognition (ASR). Being one of the most
spoken languages globally, Bengali portrays large diversity in dialects and
prosodic features, which demands ASR frameworks to be robust towards
distribution shifts. For example, islamic religious sermons in Bengali are
delivered with a tonality that is significantly different from regular speech.
Our training dataset is collected via massively online crowdsourcing campaigns
which resulted in 1177.94 hours collected and curated from $22,645$ native
Bengali speakers from South Asia. Our test dataset comprises 23.03 hours of
speech collected and manually annotated from 17 different sources, e.g.,
Bengali TV drama, Audiobook, Talk show, Online class, and Islamic sermons to
name a few. OOD-Speech is jointly the largest publicly available speech
dataset, as well as the first out-of-distribution ASR benchmarking dataset for
Bengali.