Automatic speech-based alcohol intoxication detection for automotive safety applications
Year: 2025
Type: article
Abstract: • This research is the first to report automatic alcohol detection classification using omni-microphone speaker recordings from the Alcohol Language Corpus (ALC), which less invasive and more natural than ALC headset speaker microphone recordings typically reported on in previous publications. • Five different balanced-class BAC ranges (e.g., <0.000, 0.001-0.049, 0.050-0.095, 0.100-0.149, >0.150) were evaluated to ascertain the intoxication detection robustness various speech-based feature and speech task types per BAC range. • A binary class classification ('non-intoxicated' versus 'intoxicated') approach using task-dependent recordings demonstrated accuracy improvements over the traditional task-agnostic approach; and further, cross-fold leave-one-out classification results show that task-dependent majority-vote method provided added classification performance gains. • The task-dependent majority-vote method using a relatively small number of features (320) outperformed previous published ALC automatic alcohol intoxication baselines that utilized higher-quality headset mic recordings, a higher BAC 'intoxication' class threshold (>0.05), and relied on a larger number of features (i.e., up to 4k). There is a responsibility to advance automatic alcohol intoxication screening capabilities in modern automobiles to reduce the high rate of alcohol-related accidents and fatalities worldwide. Automatic speech-based alcohol intoxication screening offers a tremendous safety opportunity in the automotive industry due to its non-invasive convenience, comparatively inexpensive cost, and rapid result processing. Using the Alcohol Language Corpus (ALC), this study examines automatic alcohol intoxication classification based on participants’ non-intoxicated/intoxicated omni-microphone speech recordings. Experimentation of many different speech features (e.g., glottal, landmarks, linguistic, prosodic, spectral, syllabic, vocal tract coordination) across different blood alcohol concentration (BAC) ranges and specific verbal tasks show significant changes as participants' BAC increases. Intoxicated participants produce lower average fundamental frequency (F0) with an increase in F0 frequency modulation, breathiness and creakiness voice qualities in intoxicated recordings when compared to their non-intoxicated recordings. For the picture description and tongue twister tasks, manual irregularity disfluency and pause linguistic features significantly increase in intoxicated recordings. Further, for all verbal tasks, automatically extracted syllabic pause features show a significant increase in intoxicated recordings. Implementation of task-dependent support vector machine classifier model with a ≥0.001 BAC 'intoxication' sensitivity threshold increases alcohol classification by up to 8% absolute gain over a task-agnostic approach. Moreover, intoxication classification results demonstrate that task-dependent modeling with majority vote decision improves classification accuracy with up to 20% absolute gain depending on task when compared to file-by-file task-agnostic method results reported previously in ALC baseline studies that used higher quality headset microphone recordings.
Source: Computer Speech & Language
Authors: Brian Stasak, Julien Epps
Institutions: The University of Sydney, UNSW Sydney
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,530
OpenAlex ID: W4413148598