High Variability Pronunciation Training (HVPT): Using Different Stimulus Talkers and Sound-To-Symbol Associations in L2 Pronunciation Learning
Bibliographic record
Abstract
This paper describes a study of High Variability Pronunciation Training (HVPT), investigating the effects of utilizing both different sound symbols and male vs. female talkers on L2 perception and production. Using the web application English Accent Coach, 8 English language learners (ELLs) were assigned to one of four groups, being trained through exposure to either male or female talkers and were asked to indicate what vowels they heard using either the International Phonetic Alphabet (IPA) or a colour grid. After hearing each sound stimulus item, learners responded by clicking on an IPA symbol or colour that represented the sound they believed that they heard and received immediate feedback. Initial descriptive statistics results based on pre- and post-tests show that participants improved in L2 vowel perception and production, regardless of which sound symbol was used as a reference. In addition, the data suggests that the sex of the stimulus talker may have a role in pronunciation acquisition for both L2 perception and production. Due to this paper’s exploratory nature, future research is needed to best inform practice and to confirm potential impacts of sex of stimulus talker in HVPT and L2 pronunciation 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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".