A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems
Name
09103574.pdf
Size
927.84 KB
Format
Adobe PDF
Checksum (MD5)
c5ae861bc7b05696c91ba13b17b61c00
Author(s)
Quatieri, Thomas F.
Talkar, Tanya
Palmer, Jeffrey S.
Date Issued
May 2020
Journal
IEEE Open Journal of Engineering in Medicine and Biology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Quatieri, Thomas et al. "A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems." IEEE Open Journal of Engineering in Medicine and Biology (May 2020)
Version
Final published version
Abstract
Goal: We propose a speech modeling and signal-processing framework to detect and track COVID-19 through asymptomatic and symptomatic stages. Methods: The approach is based on complexity of neuromotor coordination across speech subsystems involved in respiration, phonation and articulation, motivated by the distinct nature of COVID-19 involving lower (i.e., bronchial tubes, diaphragm, lower trachea) versus upper (i.e., laryngeal, pharyngeal, oral and nasal) respiratory tract inflammation [1], as well as by the growing evidence of the virus' neurological manifestations [2]—[5]. Preliminary results: An exploratory study with audio interviews of five subjects provides Cohen's d effect sizes between pre-COVID-19 (pre-exposure) from post-COVID-19 (after positive diagnosis but asymptomatic) using: coordination of respiration (as measured through acoustic waveform amplitude) and laryngeal motion (fundamental frequency and cepstral peak prominence), and coordination of laryngeal and articulatory (formant center frequencies) motion. Conclusions: While there is a strong subject-dependence, the group-level morphology of effect sizes indicates a reduced complexity of subsystem coordination. Validation is needed with larger more controlled datasets and to address confounding influences such as different recording conditions, unbalanced data quantities, and changes in underlying vocal status from pre-to-post time recordings.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Lincoln Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ojemb.2020.2998051
