Source: La Trobe UniversityLa 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.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.“A test that could simply rely on someone speaking into a microphone would be a game changer.”The algorithm was developed, and tested against, using a database dataset of 12,360 audio clips of inebriated and sober speakers.“Being able to identify intoxicated individuals solely based on their speech would be a much cheaper alternative to current systems where breath-based alcohol testing in these places is expensive and often unreliable,” Albert Bonela said.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-second recording of their speech."