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HOW TO SPOT COVID-19 PATIENTS: SPEECH & SOUND AUDIO ANALYSIS FOR PRELIMINARY DIAGNOSIS OF SARS-COV-2 CORONA PATIENTS
  • Amit Sharma,
  • Ashish Baldi,
  • Dinesh Kumar Sharma
Amit Sharma
Research Scholar, Uttarakhand Technical University, Dehradun, ISF College of Pharmacy
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Ashish Baldi
Maharaja Ranjit Singh Punjab Technical University
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Dinesh Kumar Sharma
Roorkee College of Pharmacy
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Abstract

Background: The global cases of Covid-19 increasing day by day. On Nov. 25, 2020, a total of 59,850,910 cases reported globally with a 1,411,216 global death. In India, total cases in the country now stand at 91,77,841 including 86,04,955 recoveries and 4,38,667 active cases as of Nov. 24, 2020, as per data issued by ICMR. A new generation of voice/audio analysis application which can tell whether the person is suffering from COVID-19 or not. Aims: To describe how to establish a new generation of voice/audio analysis applications to identify the suspected covid-19 hidden cases in hotspot areas with the help of an audio sample of the general public. Materials & Methods: The different patents and data available as literature on the internet are evaluated to make a new generation of voice/audio analysis application with the help of an audio sample of the general public. Results: The collection of the audio sample will be done from the already suffered covid-19 patients in (.Wave files) personally or through phone calls. The audio samples like the sound of the cough, the pattern of breathing, respiration rate, and way of speech will be recorded. The parameters will be evaluated for loudness, articulation, tempo, rhythm, melody, and timbre. The analysis and interpretation of the parameters can be made through machine learning and artificial intelligence to detect corona cases with an audio sample. Discussion: The voice/audio application current project can be merged with a mobile App called “Aarogya Setu” by Govt. of India. The project can be implemented in the high-risk area of Covid-19 in the country. Conclusion: This new method of detecting cases will decrease the workload in the covid-19 laboratory.

Peer review status:ACCEPTED

26 Nov 2020Submitted to International Journal of Clinical Practice
26 Nov 2020Assigned to Editor
26 Nov 2020Submission Checks Completed
06 Dec 2020Reviewer(s) Assigned
09 Dec 2020Review(s) Completed, Editorial Evaluation Pending
02 Mar 2021Editorial Decision: Accept