A Real-Time Robot-Based Auxiliary System for Risk Evaluation of COVID-19 Infection

Wenqi Wei, Jianzong Wang, Jiteng Ma, Ning Cheng, Jing Xiao


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.


 DOI: 10.21437/Interspeech.2020-2105

Cite as: Wei, W., Wang, J., Ma, J., Cheng, N., Xiao, J. (2020) A Real-Time Robot-Based Auxiliary System for Risk Evaluation of COVID-19 Infection. Proc. Interspeech 2020, 701-705, DOI: 10.21437/Interspeech.2020-2105.


@inproceedings{Wei2020,
  author={Wenqi Wei and Jianzong Wang and Jiteng Ma and Ning Cheng and Jing Xiao},
  title={{A Real-Time Robot-Based Auxiliary System for Risk Evaluation of COVID-19 Infection}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={701--705},
  doi={10.21437/Interspeech.2020-2105},
  url={http://dx.doi.org/10.21437/Interspeech.2020-2105}
}