Vowel-inherent spectral change enhances adaptive dispersion.
Bibliographic record
Abstract
Despite wide diversity among particular vowel sounds used across the world’s languages, there are profound systematicities across languages. Whether sets of three, five, seven, or more vowel sounds are used, vowels that comprise these sets have substantial commonality across languages. Using static measures of vowel spectra, Lindblom and colleagues have demonstrated principles of adaptive dispersion through which the compositions of vowel inventories can be predicted on the basis of maximizing perceptual distinctiveness among the vowels within a language. Here, we address whether introduction of vowel-inherent spectral change is consistent with principles of optimizing perceptual distinctiveness between vowels. We find that vowel-inherent formant trajectories generally serve to further disperse vowel sounds across time. Trajectories of formants for vowel sounds that are relatively close in static measures (formant center frequencies: beginning, center, end) tend to be relatively distinct as measured by angles in F1, F2, F3 coordinates. In a complementary fashion, vowels that share similar trajectories have relatively distinct static characteristics. This perceptual efficacy of vowel-inherent spectral change maintains across multiple place-of-articulation contexts. Across the vowel space and across consonantal contexts, vowel-inherent spectral change serves to increase adaptive dispersion and enhance perceptual distinctiveness. [Work supported by NIDCD.]
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".