Voice analysis for intoxication detection in laboratory versus law enforcement contexts
Bibliographic record
Abstract
There is substantial interest in deploying voice biomarkers for the detection of alcohol intoxication, yet it remains unknown how other mental and physical states (e.g. emotion, stress) influence voice biomarkers in intoxicated speech. We compared measurements of voice quality (jitter, shimmer, noise-harmonics ratio) across two datasets of alcohol-intoxicated speech: (1) The alcohol language corpus, which contains laboratory elicited speech from 167 individuals in both sober and intoxicated conditions and (2) a custom dataset of police (control, n = 14) and suspect (intoxicated, n = 32) interactions during traffic stops where intoxication was verified via breath analysis. Measurements were extracted from all stressed vowel tokens and compared across conditions using two-sample t-tests within sex-specific groupings of each dataset. For both males and females, jitter was significantly lower during intoxication as measured from laboratory-elicited speech, but significantly higher for intoxicated individuals when measured from police–suspect interactions. These results suggest that voice biomarkers for alcohol-intoxication are easily confounded by other emotional and physical states (e.g., stress during police interaction), and thus, present a particular challenge for speaker-independent detection systems where baseline voice quality measurements across different emotional states are unknown. [Research funded by Tenvos Incorporated for the development of commercial speaker state-detection algorithms.]
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".