Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition
Rabindra Nath Nandi Mehadi Hasan Menon Tareq Al Muntasir Sagor Sarker Quazi Sarwar Muhtaseem Md. Tariqul Islam Shammur Absar Chowdhury Firoj Alam
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)