Do Native Speakers of North American and Singapore English Differentially Perceive Comprehensibility in Second Language Speech?
Bibliographic record
Abstract
The current study examined the extent to which native speakers of North American and Singapore English differentially perceive the comprehensibility (ease of understanding) of second language (L2) speech. Spontaneous speech samples elicited from 50 Japanese learners of English with various proficiency levels were first rated by 10 Canadian and 10 Singaporean raters for overall comprehensibility and then submitted to pronunciation, fluency, vocabulary, and grammar analyses. Whereas the raters’ comprehensibility judgements were generally influenced by phonological and temporal qualities as primary cues, and, to a lesser degree, lexical and grammatical qualities of L2 speech as secondary cues, their linguistic backgrounds did make some impact on their L2 speech assessment patterns. The Singaporean raters, who not only used various models of English but also spoke a few L2s on a daily basis in a multilingual environment, tended to assign more lenient comprehensibility scores due to their relatively high sensitivity to, in particular, lexicogrammatical information. On the other hand, the comprehensibility judgements of the Canadian raters, who used only North American English in a monolingual environment, were mainly determined by the phonological accuracy and fluency of the 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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".