Quantifying the time-varying relation between speech production and postural control at different levels of vocal effort
Bibliographic record
Abstract
Speech production affects the behavior of physiological systems beyond the vocal tract. In this study, we investigate how increasing the loudness of speech production (vocal effort) affects the coupling of kinematic motions the head, body, and speech acoustics, and the relation of these coupling patterns to postural control. We predicted that increasing vocal effort would result in stronger couplings and increasingly regular kinematic and acoustic signals, reflecting an entrainment effect related to the changes in respiratory patterning associated with producing speech at high levels of vocal effort. We also predicted that postural instability would accompany this effect, since the postural control system is biomechanically coupled to the speech system via the musculature of rib cage, which takes on a more active role in respiration in high vocal effort speech. Results from six talkers who produced spontaneous speech at multiple levels of vocal effort confirmed both of these hypotheses. Stronger within-talker coupling, and the diminished behavioral variability that accompanies it, is associated with weaker coupling between the individual feet—the interface of the body with the external environment. We demonstrate this using a combination of time-series analysis methods assessing signal fluctuations (detrended fluctuation analysis) and system dimensionality (principal component analysis (PCA), correlation map analysis (CMA)). In future work, we plan to include measures of respiration in this analysis.
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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 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".