Children’s ratings of vocal emotion intensity depend on the emotion spoken and speaker familiarity but not acoustic parameters
Bibliographic record
Abstract
Scherer (1986) documented the acoustic parameters associated with adults’ perceptions of discrete vocal emotions. We investigated physical and psychological factors that influence children’s ratings of discrete vocal emotions using stimuli that approximated naturally occurring speech, including familiarity with the speaker. Specifically, we presented 52 7- and 8-year-olds with one side of a brief phone conversation spoken in happy, angry, sad, and non-emotional prosodies by both the child’s mother and another child’s mother, unfamiliar to the target child. As a group, the familiar and unfamiliar mothers’ prosodies did not differ in fundamental frequency (F0), F0 standard deviation, or speech rate—acoustic parameters that are most identified with angry, happy, and sad prosodies. Children accurately recognized the emotion spoken: They rated angry stimuli as more angry than happy or sad. Regression analyses indicated that speaker familiarity predicted children’s intensity ratings even after the target acoustic parameters were taken into account, but this effect was moderated by speaker emotion, such that children rated their mothers as more intensely angry than unfamiliar mothers. The findings suggest that their mother’s angry voice holds psychological significance for children that is not explained by variations in its most salient acoustical properties.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".