# Automatic Speech Recognition for Biomedical Data in Bengali Language

arXiv: https://arxiv.org/abs/2406.12931 · HTML: https://mlsanity.com/paper/2406.12931/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer Learning](https://mlsanity.com/paper/2209.08119/) (2022)

## Built on by (most cited first, 1 of 1)

- [BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization](https://mlsanity.com/paper/2411.10879/) (2024)

## Abstract (arXiv)

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.