A Hybrid Deep Learning Framework for Speech-to-Text Conversion as Part of Telemedicine System Integrated With 5G

Abstract

In today's world, aligning healthcare research with the third sustainable development goal of the United Nations (UN) is crucial. This goal focuses on ensuring health and well-being for all. Technological innovations like the Internet of Things (IoT) and Artificial Intelligence (AI) are vital in improving healthcare systems. Developing a technology-driven telemedicine system can have a significant impact on society. While current approaches focus on various methods for developing telemedicine modules, advancing these models with the latest technology is essential. Our paper proposes a deep learning-based framework that allows patients to provide information through voice. The system automatically analyzes this information to provide valuable insights in the doctor's dashboard, making diagnosis and prescriptions easier for the patient. Our proposed hybrid deep learning framework integrates with 5G technology and focuses on speech-to-text conversion. We introduce a hybrid deep learning model to improve performance in speech-to-text conversion. Our proposed algorithm, AI-Enabled Speech-to-Text Conversion (AIE-STTC), has the potential to match and surpass many existing deep learning models. Our empirical study, conducted using a benchmark dataset, demonstrated an impressive accuracy rate of 95.32%. In comparison, the baseline models showed lower accuracy rates: CNN achieved 88%, ResNet50 reached 90%, and VGG16 had 89%. Therefore, our proposed methodology has the potential to realize a technology-driven telemedicine system by integrating it with other necessary modules in the future. It significantly improves remote patient healthcare, making it more accessible and cost-effective, leading to a hopeful paradigm shift in healthcare services.