Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA).

Journal: Alcohol (Fayetteville, N.Y.)
PMID: 36584742

Abstract

BACKGROUND: Acute alcohol intoxication impairs cognitive and psychomotor abilities leading to various public health hazards such as road traffic accidents and alcohol-related violence. Intoxicated individuals are usually identified by measuring their blood alcohol concentration (BAC) using breathalyzers that are expensive and labor intensive. In this paper, we developed the Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA) that can instantly predict an individual's intoxication status based on a 12-s recording of their speech.

Authors

  • Abraham Albert Bonela
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia; Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
  • Zhen He
  • Aiden Nibali
    Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. anibali@students.latrobe.edu.au.
  • Thomas Norman
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Victoria, Australia.
  • Peter G Miller
    Centre for Drug Use, Addictive and Anti-social Behaviour Research, Deakin University, Geelong, Australia.
  • Emmanuel Kuntsche
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia. Electronic address: e.kuntsche@latrobe.edu.au.

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