Accounting for the accented perception of vowels: Universal preferences and language-specific biases.
Bibliographic record
Abstract
Strange things happen in cross-language and second-language vowel perception: Nave non-native listeners have been reported to rely on acoustic properties which are nonfunctional in their L1 and dysfunctional for the perception of non-native vowels; naïve non-native listeners’ perception is guided by a preference for vowels that are peripheral in the articulatory/acoustic vowel space; and, in general, naïve non-native listeners’ perception is not well predicted by comparative analyses of vowels of the native and the non-native language. This presentation reviews the accented perception of vowels by focusing on two forces which shape non-native vowel perception: universal perceptual preferences which non-native listeners (and infants) bring to the task of vowel perception, and perceptual biases which non-native listeners transfer from their native to the non-native language. Strange and her colleagues have shown that these biases cannot be predicted from acoustic comparisons; rather, they have to be examined directly through assessments of the perceived cross-language similarity of vowels. This presentation addresses several of the still unresolved questions regarding the design and the interpretation of perceptual assimilation tasks used to account for the accented perception of vowels. [Work supported by Danish Research Council for the Humanities, Canadian Natural Sciences and Engineering Research Council.]
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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 | 0.009 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".