Changes in L2 production variability associated with visual biofeedback training
Bibliographic record
Abstract
Previous research suggests that acoustic variability across repeated productions of a phoneme could index the robustness of speech motor plans. Variability at onset is thought to reflect robustness in feedforward control [1,2], while variability at midpoint may reflect the narrowness of sensory targets and/or speakers’ capacity for feedback correction [3]. In L2 production, speakers may show elevated variability at onset because of unfamiliar motor plans and at midpoint due to weak sensory targets [2]. This study investigated variability in L2 vowel production before and after a brief training incorporating visual biofeedback. We hypothesized that midpoint variability would decrease after training, reflecting refinement of the auditory target, whereas onset variability may remain unchanged because the limited training might not be sufficient to establish a robust feedforward plan. Sixty native English speakers received 1 h of biofeedback training to produce two Mandarin vowels (/y, u/). After training, both vowels showed decreased midpoint variability, but only /y/ showed reduced onset variability. In addition, for /u/ only, higher pre-training midpoint variability was correlated with greater improvement in accuracy (distance from native-speaker target). These results are discussed in connection with the differing relationships of Mandarin /y/ and /u/ to the English vowel inventory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".