Feature generalizability for speaker-dependent detection of alcohol intoxication
Bibliographic record
Abstract
Impairments to speech motor control from alcohol intoxication are variable across individuals, making speaker-dependent approaches ideal for speech-based intoxication detection [Schiel et al., 2010. Proc. INTERSPEECH 2010]. Here, we evaluated whether individual acoustic features have high generalizability across speaker-dependent models. We selected 97 speakers (54 male, 43 female) from the Alcohol Language Corpus [Schiel et al., 2012. LRE. 46, 503-521] who had sufficient sober and intoxicated (>0.08% blood-alcohol concentration) recordings for speaker-dependent modeling. For each speaker, we extracted 9 features from vowels (F0–F3, jitter, shimmer, harmonics-to-noise ratio, duration, and duration variability) and 7 from consonants (spectral skewness and kurtosis, center of gravity, duration and duration variability, harmonics-to-noise ratio), and fitted these to speaker-dependent random forest models with 5-fold cross-validation to evaluate feature importance from the associated mean decrease in Gini impurity (GI). Across all speakers, consonant-based features tended to have stronger generalizability than vowel-based features, with spectral skewness and kurtosis being the most generalizable (GI: 0.11 and 0.09), and vowel duration and F2 being the least generalizable (GI: 0.04 and 0.03). Further experiments to explore additional features and evaluate sex-specific generalizability are ongoing. [Research funded by Tenvos Incorporated for the development of commercial speaker state-detection algorithms.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".