Acoustic indicators of voice quality in the context of social support
Bibliographic record
Abstract
Is social support communicated throught the subtle, yet powerful, acoustic variations in speech? This study attempted to answer this question by testing whether acoustic parameters vary when expressing social support. Participants underwent an experiment in which they watched video testimonials of a woman describing either a neutral subject or a sensitive, emotionally-charged experience. After this, participants provided voice messages to the person appearing in the testimony. Employing the openSMILE toolkit, we extracted from these speech responses the Geneva Minimalistic Acoustic Parameter Set (GeMAPS), a set of emotion-related acoustic features. Our investigation reveals an acoustic profile characteristic of supportive speech, distinguished by changes in the Alpha ratio, spectral slope, and the Hammarberg index — parameters representing the high-frequency content and spectral balance. These acoustic differences not only help to differentiate supportive utterances but also characterize its voice quality, thereby enhancing the emotional richness of this affective stance. Our research findings have potential applications in therapeutic and communication settings and open avenues for further exploration in speech science.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 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".