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

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. ADLAIA was trained on a publicly available German Alcohol Language Corpus that comprises a total of 12,360 audio clips of inebriated and sober speakers (total of 162, aged 21–64, 47.7% female). ADLAIA's performance was determined by computing the unweighted average recall (UAR) and accuracy of inebriation prediction. ADLAIA was able to identify inebriated speakers – with a BAC of 0.05% or higher – with an UAR of 68.09% and accuracy of 67.67%. ADLAIA had a higher performance (UAR of 75.7%) in identifying intoxicated speakers (BAC > 0.12%). Being able to identify intoxicated individuals solely based on their speech, ADLAIA could be integrated into mobile applications and used in environments (such as bars, sports stadiums) to get instantaneous results about inebriation status of individuals. •ADLAIA can outperform humans in identifying alcohol-inebriated individuals based solely on 12 seconds of their speech.•ADLAIA could be integrated into mobile applications and used as a preliminary tool for identifying alcohol-inebriation.•ADLAIA was able to identify inebriated speakers – with BAC of 0.05% or higher – with an UAR of 68.09% and accuracy of 67.67%.