Effect of perceptual training without feedback on bilingual speech perception
Bibliographic record
Abstract
This study examines the effect of perceptual training without feedback on the discrimination of the Spanish stop-approximant contrasts [b]–[B], [d]–[ð], and [g]–[y]. Of interest is if the aural repetition of target stimuli improves discrimination accuracy, how many repetitions are needed to observe a significant increase in perceptual discrimination accuracy, and if there is an effect of contrast type. Perception of the Spanish target phones was assessed in adult native (L1) Spanish speakers (n = 10) and L1 English bilingual learners of L2 Spanish (n = 23) via VCV nonwords over six blocks of 10 trials, featuring Spanish approximants and voiced stops in an AX discrimination task. Results indicate a significant effect of aural stimulus repetition on discrimination accuracy scores improvement. Significant improvements were observed for all three contrasts, with discrimination gradually improving across blocks. The data indicated that the [y]–[g] contrast was the most difficult contrast and exhibited the highest rate of discrimination accuracy improvement. Discrimination accuracy score improvements were also significantly larger for L1 English listeners in comparison with the L1 Spanish listeners for this contrast.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".