Effect of auditory precision on L2 speech learning is partially mediated by learning target and instruction form
Bibliographic record
Abstract
The ability to learn a second language (L2), particularly novel speech sounds, varies significantly among individuals. Previous studies highlight the importance of auditory processing skills in L2 acquisition. However, the way auditory precision interacts with factors, such as the learning target (acoustic differences in sounds) and instruction form (types of instruction and feedback), remains to be further investigated. To explore this, we conducted a four-day classroom experiment with 80 adult Mandarin speakers learning Russian laryngeal contrasts: stops vs. fricatives. Participants were assigned to two groups receiving different instructions. One group received feedback focusing on word meaning, while the other group received instruction that emphasized both meaning and phonetic form with explicit instruction. Our findings reveal a complex interplay between auditory precision, learning target, and instruction form. Generally, superior auditory precision was associated with better speech learning outcomes across both types of instruction and consistently across learning targets. However, the three-way interaction presented a nuanced perspective. Results showed that learners with higher auditory precision benefited more from explicit instruction in their ability to perceive stop consonants; those with lower auditory precision also benefited, but to a lesser extent. This effect was not observed in the learning of fricatives. These results highlight the critical role of auditory processing in L2 learning, and suggest that instruction form may be tailored to the specific phonetic challenges faced by 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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".