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Automatic Speech Recognition for Biomedical Data in Bengali Language

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arxiv 2406.12931 v1 pith:GWLHTNWK submitted 2024-06-16 eess.AS cs.CLcs.SD

Shariar Kabir , Nazmun Nahar , Shyamasree Saha , Mamunur Rashid This is my paper · ORCID
classification eess.AScs.CLcs.SD
keywords bengalidataautomaticbiomedicalhealthcaremedicalrecognitionspeech
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This paper presents the development of a prototype Automatic Speech Recognition (ASR) system specifically designed for Bengali biomedical data. Recent advancements in Bengali ASR are encouraging, but a lack of domain-specific data limits the creation of practical healthcare ASR models. This project bridges this gap by developing an ASR system tailored for Bengali medical terms like symptoms, severity levels, and diseases, encompassing two major dialects: Bengali and Sylheti. We train and evaluate two popular ASR frameworks on a comprehensive 46-hour Bengali medical corpus. Our core objective is to create deployable health-domain ASR systems for digital health applications, ultimately increasing accessibility for non-technical users in the healthcare sector.

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  1. BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization

    cs.CL 2024-11 conditional novelty 4.0 of 10

    An ASR + MT + TTS pipeline converts Noakhali dialect speech to standard Bangla, with Whisper-large V2 achieving 0.8% CER and BanglaT5 a 41.6 BLEU on the authors' NDD dataset.

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