Perceiving prosodic prominence via unnatural visual information in avatar communication
Bibliographic record
Abstract
Listeners integrate information from simulated faces in multimodal perception [Cohen, & Massaro 1990, Behav. Res. Meth. Instr. Comp. 22(2), 260–263], but not always in the same way as real faces [Keough et al. 2017, Can. Acoust. 45(3):176–177]. This is increasingly relevant with the dramatic increase in avatar communication in virtual spaces [https://www.bloomberg.com/professional/blog/computings-next-big-thing-virtual-world-may-reality-2020/]. Prosody is especially relevant, because compared to segmental speech sounds, the visual factors indicating prosodic prominence (e.g., eyebrow raises and hand gestures) frequently bear no biomechanical relation to the production of acoustic features of prominence, but are nonetheless highly reliable [Krahmer & Swerts 2007, JML 57(3): 396–414], and avatar virtual communication systems may convey prosodic information through unnatural means, e.g., by expressing amplitude via oral aperture (louder sound = larger opening); the present study examines whether this unnatural but reliable indicator of speech amplitude is integrated in prominence perception. We report an experiment describing whether and how perceivers take into account this reliable but unnatural visual information in the detection of prosodic prominence. Preliminary evidence suggests that oral aperture increases prominence with differences by sentence position.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".