Mon-2-5-6 A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection

Abstract: In this paper, we propose a real-time robot-based auxiliary sys- tem for risk evaluation of COVID-19 infection. It combines real-time speech recognition, intent recognition, keyword de- tection, cough detection and other functions in order to convert live audio into actionable structured data to achieve the COVID- 19 infection risk assessment function. In order to better evalu- ate the COVID-19 infection, We propose an end-to-end method for cough detection and classification for our proposed systeam. It is based on real conversation data from human-robot, which processes speech signals to detect cough and classify it if de- tected. The structure of our model are maintained concise to be implemented for real-time applications. And we further em- bed this entire auxiliary diagnostic system in the robot and it is placed in the community, hospital or supermarket to facilitate people’s detection. The system can be further leveraged within a business rules engine, thus serving as a foundation for real- time supervision and assistance applications. Our model comes with a pretrained, robust training environment that allows for efficient creation and customization of customer-specific health states.