Articulatory variability is reduced by repetition and predictability
Bibliographic record
Abstract
Repeating the movements involved in activities such as drawing or sports typically leads toimprovement in kinematic behavior: associated movements become faster, smoother, and lessvariable. While practice has also been shown to lead faster and smoother movement trajectoriesin speech articulation, little is known about its effect on articulatory variability. To address this,we investigate the extent to which the repetition and predictability of articulatory gestures affectvariability in the articulation of the frequent German word ‘sie’ [zi]. We find that articulatory variabilityis proportional to the duration of [zi] and speaking rate. By contrast, we find that articulatory variabilityis reduced with repetition during the experiment. In addition, variability becomes smaller when theconditional probability of [zi] increases. The maximum reduction of variability is located during theexecution of the vocalic target of [i]. These results indicate that speakers are capable of fine tuningeven highly practiced articulatory movements.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".