Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires
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- Carmi Shimon
- Afeka College of Engineering 1 , Tel Aviv, Israel
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- Gabi Shafat
- Afeka College of Engineering 1 , Tel Aviv, Israel
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- Inbal Dangoor
- Matrix IT Ltd. 2 , Herzliya, Israel
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- Asher Ben-Shitrit
- Matrix IT Ltd. 2 , Herzliya, Israel
Description
<jats:p>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.</jats:p>
Journal
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- The Journal of the Acoustical Society of America
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The Journal of the Acoustical Society of America 149 (2), 1120-1124, 2021-02-01
Acoustical Society of America (ASA)
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Details 詳細情報について
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- CRID
- 1360580236955919360
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- ISSN
- 15208524
- 00014966
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- Data Source
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- Crossref