Exploring automatic covid-19 diagnosis via voice and symptoms from crowdsourced data
Exploring automatic covid-19 diagnosis via voice and symptoms from crowdsourced data
The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of 0.79 has been attained, with a sensitivity of 0.68 and a specificity of 0.82. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease.
COVID-19, Crowdsourced data, Speech analysis, Symptoms analysis
8328-8332
Han, Jing
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Brown, Chloë
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Chauhan, Jagmohan
831a12dc-6df9-40ea-8bb3-2c5da8882804
Grammenos, Andreas
a0e7ff5f-2149-4aab-b3e3-6733d7290659
Hasthanasombat, Apinan
94b01385-5bf6-455d-a1f9-06a9c3149ba5
Spathis, Dimitris
0352ccc2-a97c-4273-998a-0bc8546612fa
Xia, Tong
763cc71a-eaf1-4902-a27d-62cd5912a569
Cicuta, Pietro
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Mascolo, Cecilia
e4a7bcf7-72c8-43b7-b6b3-4f8980da245d
13 May 2021
Han, Jing
0e18bcab-5434-4606-b635-70fb07250322
Brown, Chloë
b65ca127-9b66-4a6e-9e3b-95c23755732d
Chauhan, Jagmohan
831a12dc-6df9-40ea-8bb3-2c5da8882804
Grammenos, Andreas
a0e7ff5f-2149-4aab-b3e3-6733d7290659
Hasthanasombat, Apinan
94b01385-5bf6-455d-a1f9-06a9c3149ba5
Spathis, Dimitris
0352ccc2-a97c-4273-998a-0bc8546612fa
Xia, Tong
763cc71a-eaf1-4902-a27d-62cd5912a569
Cicuta, Pietro
80bc9499-6c6a-4d7a-8d45-b5b3b76a4695
Mascolo, Cecilia
e4a7bcf7-72c8-43b7-b6b3-4f8980da245d
Han, Jing, Brown, Chloë, Chauhan, Jagmohan, Grammenos, Andreas, Hasthanasombat, Apinan, Spathis, Dimitris, Xia, Tong, Cicuta, Pietro and Mascolo, Cecilia
(2021)
Exploring automatic covid-19 diagnosis via voice and symptoms from crowdsourced data.
In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
IEEE.
.
(doi:10.1109/ICASSP39728.2021.9414576).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of 0.79 has been attained, with a sensitivity of 0.68 and a specificity of 0.82. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease.
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More information
Published date: 13 May 2021
Additional Information:
Publisher Copyright:
© 2021 IEEE
Venue - Dates:
2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021, , Virtual, Toronto, Canada, 2021-06-06 - 2021-06-11
Keywords:
COVID-19, Crowdsourced data, Speech analysis, Symptoms analysis
Identifiers
Local EPrints ID: 491930
URI: http://eprints.soton.ac.uk/id/eprint/491930
ISSN: 1520-6149
PURE UUID: 1aa4133e-8f2b-4837-9df0-424b2ba86729
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Date deposited: 08 Jul 2024 17:03
Last modified: 11 Jul 2024 00:10
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Contributors
Author:
Jing Han
Author:
Chloë Brown
Author:
Jagmohan Chauhan
Author:
Andreas Grammenos
Author:
Apinan Hasthanasombat
Author:
Dimitris Spathis
Author:
Tong Xia
Author:
Pietro Cicuta
Author:
Cecilia Mascolo
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