A framework for biomarkers of COVID-19 based on neuromotor coordination in speech

Thomas Quatieri
Senior Staff, Human Health and Performance Systems Group, Lincoln Laboratory

2020 SENSE.nano Symposium
Tuesday, September 22, 2020
Session 4: Populations
3:20PM – 3:35PM EST

A framework is proposed to detect and track COVID-19 based on changes in neuromotor coordination across speech subsystems involved in respiration, phonation and articulation. The approach is motivated by evidence of widespread inflammation of COVID-19 throughout the body including lower (i.e., bronchial tubes, diaphragm, lower trachea) and upper (i.e., laryngeal, pharyngeal, oral and nasal) tract injury, as well as by the growing evidence of the virus’ neurological impact. An exploratory study is described involving a small set of pre-COVID-19 (pre-exposure) versus post-COVID-19 (after positive diagnosis but presumed asymptomatic) audio interviews and a larger cohort of control versus post-COVID-19 participants in an online protocol designed by Voca.ai in collaboration with Carnegie Melon University.

For each cohort pair, Cohen’s d effect sizes were measured using coordination of respiration (as measured through the acoustic speech envelope) and laryngeal motion (fundamental frequency and cepstral peak prominence), and coordination of laryngeal and articulatory (formant center frequencies) motion. While there is a strong subject-dependence, group-level morphology of effect sizes indicates a reduced complexity of subsystem coordination. For the later (larger) cohort, an encouraging detection/false alarm tradeoff was estimated using a Gaussian mixture-based classifier. Validation is needed with larger more controlled datasets and addressing confounding influences such as different recording conditions, unbalanced data quantities, and changes in underlying vocal status from pre-to-post time recordings including changes in emotional state. 

A photo of QuatieriBiography
Thomas F. Quatieri received his B.S. degree (summa cum laude) from Tufts University in Medford, MA, and S.M., E.E., and Sc.D. degrees from the Massachusetts Institute of Technology (MIT) in Cambridge, MA. He is a senior member of the technical staff with MIT Lincoln Laboratory, Lexington, focused on speech and auditory signal processing and neuro-biophysical modeling with application to detection and monitoring of neurological, neurotraumatic, and stress conditions.

Dr. Quatieri holds a faculty appointment in the Harvard-MIT Speech and Hearing Bioscience and Technology Program. He is an author on more than 200 publications, holds 12 patents, and authored the textbook Discrete-Time Speech Signal Processing: Principles and Practice. He is a recipient of four IEEE Transactions Best Paper Awards and the 2010 MIT Lincoln Laboratory Best Paper Award. He led the Lincoln Laboratory team that won the 2013 and 2014 AVEC Depression Challenges and the 2015 MIT Lincoln Laboratory Team Award for their work on vocal and facial biomarkers. He served on many IEEE technical committees and the IEEE James L. Flanagan Speech and Audio Awards Committee. He has also served on the editorial board of the IEEE Transactions on Signal Processing, and is currently an associate editor of Computer, Speech, and Language. He co-led the mHealth group on Monitoring COVID Patients and Clinical Personnel under the Mass General Brigham Center for COVID Innovation. He is a Fellow of the IEEE and a member of Tau Beta Pi, Eta Kappa Nu, Sigma Xi, ICSA, and ASA.