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• W3137914271 abstract "Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82-86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity." @default.
• W3137914271 created "2021-03-29" @default.
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• W3137914271 date "2021-03-11" @default.
• W3137914271 modified "2023-10-17" @default.
• W3137914271 title "Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis" @default.
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• W3137914271 doi "https://doi.org/10.1007/s41666-020-00090-4" @default.
• W3137914271 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7948650" @default.
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