THE MUTUAL INTELLIGIBILITY OF L2 SPEECH
Bibliographic record
Abstract
When understanding or evaluating foreign-accented speech, listeners are affected not only by properties of the speech itself but by their own linguistic backgrounds and their experience with different speech varieties. Given the latter influence, it is not known to what degree a diverse group of listeners might share a response to second language (L2) speech. In this study, listeners from native Cantonese, Japanese, Mandarin, and English backgrounds evaluated the same set of foreign-accented English utterances from native speakers of Cantonese, Japanese, Polish, and Spanish. Regardless of native language background, the listener groups showed moderate to high correlations on intelligibility scores and comprehensibility and accentedness ratings. Although some between-group differences emerged, the groups tended to agree on which of the 48 speakers were the easiest and most difficult to understand; between-group effect sizes were generally small. As in previous studies, the listeners did not consistently exhibit an intelligibility benefit for speech produced in their own accent. These findings support the view that properties of the speech itself are a potent factor in determining how L2 speech is perceived, even when the listeners are from diverse language backgrounds.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".