  One of the major challenges for developing automatic speech recognition (ASR)
for low-resource languages is the limited access to labeled data with
domain-specific variations. In this study, we propose a pseudo-labeling
approach to develop a large-scale domain-agnostic ASR dataset. With the
proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset
covering diverse topics, speaking styles, dialects, noisy environments, and
conversational scenarios. We then exploited the developed corpus to design a
conformer-based ASR system. We benchmarked the trained ASR with publicly
available datasets and compared it with other available models. To investigate
the efficacy, we designed and developed a human-annotated domain-agnostic test
set composed of news, telephony, and conversational data among others. Our
results demonstrate the efficacy of the model trained on psuedo-label data for
the designed test-set along with publicly-available Bangla datasets. The
experimental resources will be publicly
available.(https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR)
