  In this paper, we propose a real-time robot-based auxiliary system for risk
evaluation of COVID-19 infection. It combines real-time speech recognition,
temperature measurement, keyword detection, 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 evaluate the
COVID-19 infection, we propose an end-to-end method for cough detection and
classification for our proposed system. It is based on real conversation data
from human-robot, which processes speech signals to detect cough and classifies
it if detected. The structure of our model are maintained concise to be
implemented for real-time applications. And we further embed this entire
auxiliary diagnostic system in the robot and it is placed in the communities,
hospitals and supermarkets to support COVID-19 testing. 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 utilizes a
pretrained, robust training environment that allows for efficient creation and
customization of customer-specific health states.
