Range- and domain-specific exaggeration of facial speech
Bibliographic record
Abstract
Is it possible to exaggerate the different ways in which people talk, just as we can caricature their faces? In this paper, we exaggerate animated facial movement to investigate how the emotional manner of speech is conveyed. Range-specific exaggerations selectively emphasized emotional manner whereas domain-specific exaggerations of differences in duration did not. Range-specific exaggeration relative to a time-locked average was more effective than absolute exaggeration of differences from the static, neutral face, despite smaller absolute differences in movement. Thus, exaggeration is most effective when the average used captures shared properties, allowing task-relevant differences to be selectively amplified. Playing the stimuli backwards showed that the effects of exaggeration were temporally reversible, although emotion-consistent ratings for stimuli played forwards were higher overall. Comparison with silent video showed that these stimuli also conveyed the intended emotional manner, that the relative rating of animations depends on the emotion, and that exaggerated animations were always rated at least as highly as video. Explanations in terms of key frame encoding and muscle-based models of facial movement are considered, as are possible methods for capturing timing-based cues.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".