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

Abraham Albert Bonela¹, Zhen He², Aiden Nibali²

¹Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia; Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.

Alcohol (Fayetteville, N.Y.)

|December 30, 2022

Summary

This summary is machine-generated.

A new Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA) can detect intoxication from speech. This technology offers instant, non-invasive alcohol impairment detection, potentially reducing public health risks.

Keywords:

Artificial intelligence Audio Deep learning Inebriation detection Speech

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Area of Science:

Artificial Intelligence
Machine Learning
Speech Analysis

Background:

Acute alcohol intoxication significantly impairs cognitive and psychomotor functions, contributing to public health issues like accidents and violence.
Current methods for identifying intoxicated individuals, such as breathalyzers, are often costly and labor-intensive.
There is a need for rapid, accessible methods to assess alcohol impairment.

Purpose of the Study:

To develop and evaluate an AI-powered algorithm for detecting alcohol inebriation using speech analysis.
To assess the algorithm's accuracy in identifying individuals with varying levels of blood alcohol concentration (BAC).

Main Methods:

Development of the Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA).
Training ADLAIA on a dataset of 12,360 audio clips from 162 sober and inebriated German speakers.
Performance evaluation using unweighted average recall (UAR) and accuracy metrics.

Main Results:

ADLAIA achieved 68.09% UAR and 67.67% accuracy in identifying speakers with a BAC of 0.05% or higher.
The algorithm demonstrated higher performance, with a 75.7% UAR, in detecting more heavily intoxicated speakers (BAC > 0.12%).

Conclusions:

Speech-based alcohol inebriation detection is feasible using deep learning.
ADLAIA offers a potential for instant, non-invasive assessment of alcohol impairment.
The algorithm could be integrated into mobile applications or used in public venues for real-time inebriation status checks.