Deep learning algorithm can hear alcohol in voice

La Trobe University researchers have developed an artificial intelligence (AI) algorithm that could work alongside expensive and potentially biased breath testing devices in pubs and clubs. The technology can instantly determine whether a person has exceeded the legal alcohol limit purely on using a 12-seconds recording of their voice. In a paper published in the journal Alcohol, the study led by Ph.D. student Abraham Albert Bonela and supervised by Professors Emmanuel Kuntsche and Associate Professor Zhen He, from the Center for Alcohol Policy Research and the Department of Computer Science and Information Technology at La Trobe University, respectively, describes the development of the Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA) that can determine an individual's intoxication status based on a 12-second recording of their speech. "Intoxicated individuals are usually identified by measuring their blood alcohol concentration (BAC) using breathalyzers that are expensive and labor-intensive," Albert Bonela said. "A test that could simply rely on someone speaking into a microphone would be a game changer."