Detection of Alcohol Intoxication Using Voice Features: A Controlled Laboratory Study
Bibliographic record
Abstract
OBJECTIVE: Devices such as mobile phones and smart speakers could be useful to remotely identify voice alterations associated with alcohol intoxication that could be used to deliver just-in-time interventions, but data to support such approaches for the English language are lacking. In this controlled laboratory study, we compare how well English spectrographic voice features identify alcohol intoxication. METHOD: A total of 18 participants (72% male, ages 21-62 years) read a randomly assigned tongue twister before drinking and each hour for up to 7 hours after drinking a weight-based dose of alcohol. Vocal segments were cleaned and split into 1-second windows. We built support vector machine models for detecting alcohol intoxication, defined as breath alcohol concentration > .08%, comparing the baseline voice spectrographic signature to each subsequent timepoint and examined accuracy with 95% confidence intervals (CIs). RESULTS: Alcohol intoxication was predicted with an accuracy of 98% (95% CI [97.1, 98.6]); mean sensitivity = .98; specificity = .97; positive predictive value = .97; and negative predictive value = .98. CONCLUSIONS: In this small, controlled laboratory study, voice spectrographic signatures collected from brief recorded English segments were useful in identifying alcohol intoxication. Larger studies using varied voice samples are needed to validate and expand models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".