BAND: A Benchmark Dataset forBangla News Audio Classification

Abstract

Despite being the sixth most widely spoken language in the world, Bangla has barely received any attention in the domain of audio-visual news classification. In this work, we collect, annotate, and prepare a comprehensive news audio dataset in Bangla, comprising 5120 news clips, with around 820 hours of total duration. We also conduct practical experiments to obtain a human baseline for the news audio classification task. Later, we implement one of the human approaches by performing news classification directly on the audio features using various state-of-the-art classifiers and a few transfer learning models. To the best of our knowledge, this is the very first work developing a benchmark dataset for news audio classification in Bangla.