Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition

• URL: http://arxiv.org/abs/2311.03196v1
• Date: Mon, 6 Nov 2023 15:37:14 GMT
• Title: Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition
• Authors: Rabindra Nath Nandi, Mehadi Hasan Menon, Tareq Al Muntasir, Sagor Sarker, Quazi Sarwar Muhtaseem, Md. Tariqul Islam, Shammur Absar Chowdhury, Firoj Alam
• Abstract summary: In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. We developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets.
• Score: 10.244515100904144
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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)

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