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AI-Based human audio processing for COVID-19: A comprehensive overview.
Deshpande, Gauri; Batliner, Anton; Schuller, Björn W.
  • Deshpande G; Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
  • Batliner A; TCS Research Pune, India.
  • Schuller BW; Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
Pattern Recognit ; 122: 108289, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1377809
ABSTRACT
The Coronavirus (COVID-19) pandemic impelled several research efforts, from collecting COVID-19 patients' data to screening them for virus detection. Some COVID-19 symptoms are related to the functioning of the respiratory system that influences speech production; this suggests research on identifying markers of COVID-19 in speech and other human generated audio signals. In this article, we give an overview of research on human audio signals using 'Artificial Intelligence' techniques to screen, diagnose, monitor, and spread the awareness about COVID-19. This overview will be useful for developing automated systems that can help in the context of COVID-19, using non-obtrusive and easy to use bio-signals conveyed in human non-speech and speech audio productions.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study Language: English Journal: Pattern Recognit Year: 2022 Document Type: Article Affiliation country: J.patcog.2021.108289

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study Language: English Journal: Pattern Recognit Year: 2022 Document Type: Article Affiliation country: J.patcog.2021.108289