Automatic and manual extraction of speech and language characteristics associated with depression
Bibliographic record
Abstract
Early and accurate diagnosis and intervention of depression is important to facilitate timely, direct, and appropriate interventions with potential for improved clinical outcomes. Delays in the diagnosis of patients with depression may be reduced if simple tools were available to indicate probability of diagnosis. Clinicians use speech and language characteristics to establish current mental state and diagnosis. The use of automatic acoustic feature extraction allows leveraging pitch, power, and variability and can provide an unbiased evaluation of speech. This study examined speech samples from a youth at-risk cohort, aged 9-25, and developed a manual rating system of speech and language characteristics, which involved rating short segments of audio and transcript on emotion, sentiment, affect, and richness. This competed against an automated model of extracting zero-crossing rate, energy, the entropy of energy, and Mel-frequency cepstral coefficients to identify speech characteristics associated with major depressive disorder. The results showed that the automatic feature extraction outcompeted the manual rating system in explaining the difference in speech between participants with and without major depressive disorder through speech and language characteristics. While the extraction of audio features is not a substitute for the clinical interview, the ability to provide an unbiased prediction of vulnerability to depression from speech may assist clinicians in early diagnosis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".