The effect of phonetic context on speech movements in repetitive speech
Bibliographic record
Abstract
This study examined how, in repetitive speech, articulatory movements differ in degree of variability and movement range depending on articulatory constraints manipulated by phonetic context and type of CVC-CVC word pair. These pairs consisted of words that either differed in onset consonants but shared rhymes, or were identical. Articulatory constraints were manipulated by employing different combinations of vowels and consonants. The word pairs were produced in a repetitive speech task at a normal and fast speaking rate. Articulatory movements were measured with 3D electro-magnetic articulography. As measures of variability, median movement ranges and the coefficient of variation of target and non-target articulators were determined. To assess possible biomechanical constraints, correlation values between target and simultaneous non-target articulators were calculated as well. The results revealed that word pairs with different onsets had larger movement ranges than word pairs with identical onsets. In identical word pairs, the coefficient of variation showed higher values in the second than in the first word. This difference was not present in the alternating onset word pairs. For both types of word pairs, higher speaking rates showed higher correlations between target and non-target articulators than lower speaking rates, suggesting stronger biomechanical constraints for the former condition.
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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 | 0.000 | 0.005 |
| 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 | 0.001 |
| 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".