Affricate-fricative perception in Korean listeners: Evidence for universal and language specific biases
Bibliographic record
Abstract
Although language experience has a profound impact on phonetic perception, there is increasing evidence that phonetic perception is also shaped by universal biases which can be revealed as asymmetries in discrimination performance. In the present study, we explore potential perceptual asymmetries in adult Korean perception of four English affricate-fricative contrasts. Korean adults completed a native-language assimilation task and a category-based AX discrimination task with the phonemic contrast /tʃa-sa/ and non-phonemic contrasts /tʃa-ʃa/, /dʒa-za/ and /dʒa-ʒa/. Both voiceless contrasts—/tʃa-sa/ and /tʃa-ʃa/—were assimilated to distinct Korean affricate and fricative categories and were discriminated very well (>90%); performance revealed no perceptual asymmetries. Both voiced contrasts—/dʒa-za/ and /dʒa-ʒa/—were assimilated to the same Korean affricate category (/tʃa/) and were poorly discriminated (63-65%); performance was asymmetric on different pairs for both contrasts (fricative-affricate pairs>affricate-fricative pairs) and on same pairs for /dʒa-ʒa/ (fricative-fricative pairs>affricate-affricate pairs). These findings, and prior research, show that asymmetrical performance on different pairs is highly uniform and predicted by phone type, pointing to a possible universal bias favoring sharp amplitude onsets. However, asymmetries in same pair performance are predicted by language categorization and thus appear to be shaped by language-specific experience.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".