Does perceptual high variability phonetic training improve L2 speech production? A meta-analysis of perception-production connection
Bibliographic record
Abstract
Abstract This meta-analysis of 31 studies aimed to determine the effectiveness of perception-based high variability phonetic training (HVPT) for second language (L2) production learning and to identify learner-related and methodological variables that influence production gains. Based on independent effect sizes for 43 within-participant and 17 between-participant designs, small-to-medium effects of post-training improvement were found. The average production gains for trained items and untrained items were 10.50% and 4.50%, respectively. Neither strong support for long-term retention of production learning nor generalization to untrained stimuli was observed, however. Moderator analyses showed that post-training production gains were influenced by a number of factors related to learner profiles (age and learning context), training features (provision of phonetic information, training duration, and training time per session), and features of production tests (elicitation tasks, prompt modality, and outcome measures). The relationship between perception and production gains was negligible at the participant level, but was significant and moderate at the level of individual studies for post-training and retention data. These findings provide partial support for a perception-production link. This study makes several recommendations for future studies investigating the effects of HVPT on L2 speech production learning.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.025 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".