Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns.

ABSTRACT: In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of a

SUBMITTER: Talkar T 

PROVIDER: S-EPMC9884222 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature