Diachronic change in perception of Korean sibilants
Bibliographic record
Abstract
The laryngeal contrast in the Seoul dialect of Korean is in a state of flux: the increasing importance of f0 (relative to VOT) in both perception and production of the three-way stop contrast in younger (vs. older) Seoul speakers has been well-documented. The current work turns to perception of the Korean sibilant series, comprised of a three-way affricate contrast, (fortis vs. lenis vs. aspirated, parallel to the stop contrast) and a phonologically ambiguous two-way fricative contrast (fortis vs. “nonfortis”). We map younger (mean 33 years old) and older (mean 66) Seoul listeners’ perceptual spaces for the sibilant class using a five-way forced-choice task, with stimuli manipulated to vary independently across multiple acoustic dimensions (consonantal spectral information, vocalic spectral information, frication duration, aspiration duration, and f0). Hierarchical classification tree analyses reveal systematic age-related differences in cue-weighting. While both age groups make use of a combination of spectral properties of both the consonant and vowel, temporal information, and f0 when categorizing the stimuli, f0 plays a greater role in predicting sibilant classification in younger as compared to older listeners. Furthermore, categorization patterns suggest that the sound change not only affects perception of the three-way laryngeal contrast, but also has implications for other phonological contrasts (e.g. affricate vs. fricative “manner” contrast).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".