Communications of the ACM
Deep Learning Algorithm Can Hear Alcohol in Voice
La Trobe Universit's Abraham Albert Bonela observed that intoxicated individuals are usually identified by measuring their blood alcohol concentration; "A test that could simply rely on someone speaking into a microphone would be a game changer."
Credit: La Trobe University
Researchers at Australia's La Trobe University have developed an algorithm that can instantly determine whether a person has exceeded the legal alcohol limit based on a 12-second recording of that person's voice.
The Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA) was developed with, and tested against, a dataset of 12,360 audio clips of inebriated and sober speakers.
ADLAIA was able to identify inebriated speakers having a blood alcohol content (BAC) of 0.05% or higher with an accuracy of nearly 70%, which climbed to 76% for speakers with a BAC of more than 0.12%.
Said La Trobe's Abraham Albert Bonela, "Upon further improvement in its overall performance, ADLAIA could be integrated into mobile applications and used as a preliminary tool for identifying alcohol-inebriated individuals."
From La Trobe University (Australia)
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