Deep learning based large vocabulary continuous speech recognition of an under-resourced language Bangladeshi Bangla

Ahnaf Mozib Samin, M. Humayon Kobir, Shafkat Kibria, M. Shahidur Rahman

Sept 2021


Research in corpus-driven Automatic Speech Recognition (ASR) is advancing rapidly towards building a robust Large Vocabulary Continuous Speech Recognition (LVCSR) system. Under-resourced languages like Bangla require benchmarking large corpora for more research on LVCSR to tackle their limitations and avoid the biased results. In this paper, a publicly published large-scale Bangladeshi Bangla speech corpus is used to implement deep Convolutional Neural Network (CNN) based model and Recurrent Neural Network (RNN) based model with Connectionist Temporal Classification (CTC) loss function for Bangla LVCSR. In experimental evaluations, we find that CNN-based architecture yields superior results over the RNN-based approach. This study also emphasizes assessing the quality of an open-source large-scale Bangladeshi Bangla speech corpus and investigating the effect of the various high-order N-gram Language Models (LM) on a morphologically rich language Bangla. We achieve 36.12% word error rate (WER) using CNN-based acoustic model and 13.93% WER using beam search decoding with 5-gram LM. The findings demonstrate by far the state-of-the-art performance of any Bangla LVCSR system on a specific benchmarked large corpus.

Type Journal article

Publication Acoustical Science and Technology

Publisher The Acoustical Society of Japan



title={Deep learning based large vocabulary continuous speech recognition of an under- resourced language Bangladeshi Bangla},

author={Samin, Ahnaf Mozib and Kobir, M Humayon and Kibria, Shafkat and Rahman, M Shahidur},

journal={Acoustical Science and Technology},





publisher={Acoustical Society of Japan}