Is It Because of My Language Background? A Study of Language Background Influence on Comprehensibility Judgments
Bibliographic record
Abstract
This study examines the role of listeners’ native language (L1) background in judgments of comprehensibility (ease of understanding) for speakers from same and different L1 backgrounds, to determine the extent of a shared second language (L2) comprehensibility benefit. Forty L2 English speakers from Mandarin, French, Hindi, and English backgrounds (10 per group) listened to speech samples from 30 L2 English speakers from Mandarin, French, and Hindi backgrounds (10 per group). Listeners first evaluated each speaker’s comprehensibility and provided verbal reports indicating their reasons for each rating. To estimate pronunciation influences on comprehensibility, listeners then rated each speaker for four speech measures (segmental and word stress errors, intonation, speech rate). Results revealed that different speech measures were associated with comprehensibility ratings for different listener–speaker groups, and that a match in L1 background accounted for additional unique variance in comprehensibility ratings, but only for the Mandarin listeners and speakers. Verbal reports indicated that listeners more often considered L1 a benefit when rating speakers from their own L1 and a detriment when evaluating speakers from a different L1. Findings overall point to small effects of shared L1 background on comprehensibility, suggesting alternative priorities for teaching and researching comprehensible L2 speech.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".