TelMedAI: A Framework for Patient Speech Recognition and Conversion into Desired Language Towards Telemedicine System - Owk, Mrudula; Godavarthi, Deepthi; Sindhu, Pusarla; Mohana, T. Krishna (International Journal of Electrical and Computer Engineering Systems, 2024)
Telemedicine is the practice of technology-enabled remote communication between patient and doctor. This phenomenon in healthcare has the potential to make services affordable and save time and money. Besides telemedicine allows care givers and family members to join conversations with doctors. Indian government initiated the National Telemedicine Network (NTN) to serve remote areas in healthcare by integrating existing healthcare facilities.Literature has revealed that existing works lack in an integrated approach for patient speech translation in language-independent fashion and automatic detection of disease and symptoms based on speech.There is a need for an automated system using Artificial Intelligence (AI) to recognize patient’s speech and identify symptoms based on given audio description. We proposed a framework known as TelMedAI which is designed to recognize patient speech to comprehend disease symptoms besides converting the speech text into desired language. The framework is useful for realizing a telemedicine system. Speech to Speech (STS) module takes the patient’s audio content into English audio. STS module exploits the Bi-LSTM model with an encoder, decoder and attention mechanism for translation. Then Google Speech API is used to convert English audio into English text. Then the framework exploits Natural Language Processing (NLP) to improve the quality of text. Afterwards, the disease and symptoms miner module eventually recognizes a list of diseases and corresponding symptoms. We proposed an algorithm known as Learning based Disease and Symptom Recognition from Patient Speech (LbDSRPS). This algorithm has the functionality to develop TelMedAI which helps doctors in telemedicine. Our empirical study has revealed that TelMedAI takes technology-driven telemedicine research forward significantly. The highest accuracy achieved by the proposed framework is 68.13% which is much better than the baseline LSTM model used for voice translation.