Audiovisual high variability phonetic training promotes second language lexical tone learning: An event-related potential study
Bibliographic record
Abstract
This study investigated the efficacy of a modified high variability phonetic training (HVPT) protocol in second language learning. The target Mandarin lexical tones in the training stimuli were acoustically exaggerated at four levels in terms of duration, pitch range, and pitch contour. Seven audiovisual perceptual training sessions were designed to deliver the training stimuli adaptively based on the listener's identification score. Pre- and post-tests used behavioral identification and discrimination tasks with natural speech, synthetic speech, and non-speech control stimuli. ERP experiments were also conducted with synthetic speech stimuli in a passive listening oddball paradigm. A total of 24 adult monolingual American English speakers were randomly assigned to the training group and control group. Behavioral data showed significant improvement in identifying the four lexical tones in the trainees but not in the controls. There was also training-induced enhancement of categorical perception of the lexical tones for the synthetic speech stimuli. Posttest versus pretest comparison in the trainees showed an increased mismatch negativity (MMN) response with decreased MMN peak latency for the across-category lexical tone stimuli. Collectively, the results demonstrate fundamental neural sensitivity changes at the pre-attentive level underlying training-induced behavioral identification and discrimination changes in second language learners.
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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 | 0.000 |
| 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".