Effects of motor practice on the temporal coordination of articulatory movements for non-native onset clusters: Kinematic and acoustic evidence using electromagnetic articulography
Bibliographic record
Abstract
Research on cross-language speech production has shown that part of the challenge of producing non-native clusters arises from difficulties with temporally coordinating the successive consonantal gestures within a cluster. However, it remains unclear whether the application of practice-based motor learning paradigms can improve or stabilize this aspect of non-native cluster production. This study uses electromagnetic articulography (EMA) to measure the effects of motor practice on the temporal coordination of articulatory movements for non-native onset clusters. Monolingual speakers of American English intensively practiced producing monosyllabic sequences containing non-native onset clusters (e.g., MGAT) over two consecutive days. EMA was used to capture lingual, labial, and jaw motion during successive repetitions. For properly-sequenced cluster repetitions, analysis of the EMA sensor trajectories showed that, over the course of practice, there was a general reduction in inter-gestural timing variability and increased overlap between adjacent consonantal gestures. Furthermore, some of these improvements were maintained to the second day, indicating that subjects started forming durable performance gains. Acoustic analyses also showed a reduction in the duration of epenthetic vowel errors (/mgæt/ → /məgæt/) produced throughout practice. Collectively, the findings suggest that motor practice can improve the temporal coordination of articulatory gestures affiliated with non-native clusters.
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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 | 0.003 |
| 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 | 0.000 | 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".