Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires.
J Acoust Soc Am
; 149(2): 1120, 2021 02.
Article
in English
| MEDLINE | ID: covidwho-1153607
ABSTRACT
The COVID-19 outbreak was announced as a global pandemic by the World Health Organization in March 2020 and has affected a growing number of people in the past few months. In this context, advanced artificial intelligence techniques are brought to the forefront as a response to the ongoing fight toward reducing the impact of this global health crisis. In this study, potential use-cases of intelligent speech analysis for COVID-19 identification are being developed. By analyzing speech recordings from COVID-19 positive and negative patients, we constructed audio- and symptomatic-based models to automatically categorize the health state of patients, whether they are COVID-19 positive or not. For this purpose, many acoustic features were established, and various machine learning algorithms are being utilized. Experiments show that an average accuracy of 80% was obtained estimating COVID-19 positive or negative, derived from multiple cough and vowel /a/ recordings, and an average accuracy of 83% was obtained estimating COVID-19 positive or negative patients by evaluating six symptomatic questions. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Voice
/
Artificial Intelligence
/
Surveys and Questionnaires
/
Cough
/
Cues
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Qualitative research
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
J Acoust Soc Am
Year:
2021
Document Type:
Article
Affiliation country:
10.0003434
Similar
MEDLINE
...
LILACS
LIS