Short- and Medium-Term Effects of Mouth Movement-Focused Instruction on English Linking Pronunciation
Bibliographic record
Abstract
Visual cues such as mouth movements have gained attention in English pronunciation instruction, yet they have mainly targeted individual sounds (e.g., /l/ and /r/), with limited application to connected speech features such as rhythm and linking. In this study I investigated the short- and mid-term effects of mouth movement-focused instruction on English linking. I developed a single 24-minute on-demand lesson using video footage that clearly demonstrated native English speakers’ lip and tongue movements. Linking performance was measured at three points—before instruction, immediately after the instruction, and at the end of the semester. Results showed that even this brief intervention led to noticeable improvement in learners’ production of linking, especially at the phrase level. Learners also reported enhanced awareness of overall pronunciation through the visual instruction. However, performance in sentence-level linking was less stable, with some targets showing decline over time. These findings suggest that while short, visually guided instruction can be effective in raising awareness and improving production, repeated practice and sustained exposure are necessary for long-term retention and fluency development.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".