A kinematic analysis of visual prosody: Head movements in habitual and loud speech
Bibliographic record
Abstract
Prosodic prominence manifests itself in intonation, timing and magnitude of supra-laryngeal articulation as well as speech-accompanying gestures. The inter- play of prosody and gesture has been described as 'visual prosody' and is known to play an important role in communication. However, few studies have investigated visual prosody across different speaking styles. In this study, we examine co-speech head mo- tion related to prosodic prominence in habitual and loud speech. The results show overall differences be- tween speaking styles as well as some signatures of prosodic prominence, which are stronger in loud than in habitual speech. The paper underlines the potential of a fine-grained kinematic approach to explore con- tinuous speech-accompanying movements. cite as:Pagel, L., Sóskuthy, M., Roessig, S., & Mücke, D. (2023). A kinematic analysis of visual prosody: Head movements in habitual and loud speech. In Skarnitzl, R., & Volín, J. (Eds.), Proceedings of the 20th International Congress of Phonetic Sciences, 7-11 August, Prague, Czech Republic (pp. 4130–4134). Guarant International. DOI: 10.5281/zenodo.10299230.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".