A recent study, covered by The Guardian and published in the Journal of Studies on Alcohol and Drugs, suggests that artificial intelligence could soon be used to assess sobriety based on how well someone recites tongue twisters. In this intriguing experiment, 18 legal-age adults were given vodka gimlets until they reached varying levels of intoxication. They were then asked to recite tongue twisters every hour, with their breath alcohol levels being monitored at thirty-minute intervals.
The study observed notable changes in voice pitch and frequency correlating with different levels of drunkenness. Leveraging these observations, researchers trained an AI program to analyze the speech patterns. Impressively, the AI was able to predict with 98% accuracy whether someone was within the legal sobriety limits for driving. Dr. Brian Suffoletto, the lead author of the study and an associate professor of emergency medicine at Stanford, expressed optimism about the practical applications of this technology. He told the Register that, given the advancements in smartphone sensors, digital signals could be harnessed to predict drinking episodes more accurately, thereby allowing timely interventions.
One potential application of this technology, as Suffoletto noted, could be an ignition lock for vehicles. This system would require drivers to pass a ‘voice challenge’ before they could start their car. This could also be implemented in high-risk workplaces, like school bus driving or heavy machine operation, to ensure public safety. Furthermore, he suggested that restaurants and bars could use similar devices to determine when to stop serving alcohol to patrons.
However, the study’s limitations are notable. It had a small sample size and lacked diversity, as all participants were white. Petra Meier, a professor of public health, commented on the potential of this approach but emphasized the need for further testing in larger and more diverse groups. “I believe that there is the potential for exciting developments that could eventually be really useful,” she said, underlining the necessity of comprehensive trials before such technology could be implemented widely.
