High variability phonetic training (HVPT): A meta-analysis of L2 perceptual training studies
Bibliographic record
Abstract
Abstract This meta-analysis of 79 studies evaluates the effectiveness of high variability phonetic training (HVPT) for the development of second language (L2) speech perception and explores learner-related and methodological variables that influence training effects. The overall medium-to-large effects of HVPT on L2 speech perception support the effectiveness of HVPT, for both pretest-posttest comparison (g = 0.92, k = 96) and treatment-control comparison (g = 0.67, k = 32), confirm long-term retention of perception gains, and, to some extent, indicate generalization of learning to novel stimuli. Training effects are influenced by several key variables (length of L2 learning, response labels, type of training task, type of testing task, total training time, target phones, and number of talkers). The findings provide compelling evidence to support the efficacy of HVPT for L2 perceptual learning and suggest circumstances under which training effects are optimized.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.003 | 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".