Bangladeshi Bangla Speech Corpus for Automatic Speech Recognition Research

Shafkat Kibria, Ahnaf Mozib Samin, M. Humayon Kobir, M. Shahidur Rahman, M. Reza Selim, M. Zafar Iqbal

Jan 2022

Abstract

This article reports the development of language resource for Bangladeshi Bangla spoken language (BBSL). Bangladeshi Bangla has inadequate large speech corpora for Large Vocabulary Continuous Speech Recognition (LVCSR) system. The accuracy of the automatic speech recognition (ASR) system rests on the quality of the speech corpus. This work discusses the common issues and activities related to the development of a large speech corpus named (SUBAK.KO). This corpus is designed to support ASR research in Bangladeshi Bangla. It has been labeled sentence-wise. We have trained this corpus with one of the well-known current End-to-End ASR algorithms, Recurrent Neural Networks (RNNs) with Connectionist Temporal Classification (CTC). To know the strengths and weaknesses, the CER (Character Error Rate) and the WER (Word Error Rate) of the trained RNNCTC model have been observed. Another open-source large Bangla ASR corpus has been trained using the same ASR algorithm. Both trained models have been compared to assess the quality of these corpora. It has been found that SUBAK.KO is a more balanced corpus and considered more regional accented speech variability for a LVCSR system compared to that open-source large Bangla ASR corpus.

Type Journal article

Publication Speech Communication

Publisher Elsevier