New effects of laryngeal configurations on <i>f</i>0: Voiceless stops in Korean
Bibliographic record
Abstract
Korean has three sets of voiceless stops in word-initial position which are distinctive from one another in terms of laryngeal adjustments: lenis unaspirated, fortis unaspirated, and aspirated [P. Ladefoged and I. Maddiedson, 47–101 (1996)]. It has been observed that, in Standard Korean, the aspirated stops have higher f0 value than the fortis stops, while both stops have higher f0 value than the lenis stops [D. Silva, 11–34 (1998)]. It could be argued that this indicates a universal correspondence between laryngeal configurations for voicing and pitch—at least within Korean. However, the current paper reveals a different observation. In South Kyengsang dialect in Korean, little difference is found in the f0 value between the aspirated and the fortis stops, while the f0 of the lenis stops is relatively lower, similar to Silva’s (1998) finding. A total of 600 tokens, by five speakers, were recorded and digitized, then analyzed using Macquirer software for pitch tracking. The results show that the f0 following the fortis stops are consistently higher than that of Standard Korean, regardless of the place of articulation, and the higher f0 of the fortis stops patterns with the lower f0 of the lenis stops among speakers.
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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 | 0.002 |
| 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) | 0.003 | 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".