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COVID-19 detection based on respiratory sensing from speech

COVID-19 affects a person's respiratory health, which is manifested in the form of shortness of breath during speech. Recent work shows that it is possible to use deep learning techniques to sense the speaker's respiratory parameters from a speech signal directly. Thus respiratory parameters like speech breathing rate and tidal volume can be computed and compared using deep learning techniques to detect COVID-19 from speech recordings. In this paper, we compute respiratory parameters using our pre-trained deep learning-based speech breathing models and use them for detecting COVID-19 from speech. Apart from using speech breathing models, we perform acoustic features identification using a statistical approach and classification based on low-level descriptive features. Our analysis investigates the distinction of speech of a healthy person and COVID-19 affected person. Copyright © 2022 ISCA.