Natural referent vowels guide the development of vowel perception
Bibliographic record
Abstract
Certain vowels are favored across languages of the world. This selection bias has received a great deal of attention in linguistic theories seeking to explain vowel system typologies. In comparison, the role that specific vowels might play in the ontogeny of vowel perception has been more implicit. In this talk we will summarize recent findings that elucidate the functional significance of peripheral vowels in the development of vowel perception. Data from cross-language studies of infant vowel discrimination and vowel preference will be presented. This work shows that peripheral vowels have a perceptual priority for young infants and that this bias is independent of the phonemic status of the vowels presented in the perceptual task. Findings from cross-language experiments with adults reveal that language experience builds on the natural vowel biases observed in infancy. Adult data suggest that the natural bias remains in place in mature listeners unless the perceiver needs to override the bias to optimize perception of functional vowel differences. These findings support our proposal of a Natural Reference Vowel hypothesis as a framework for understanding the development of vowel perception and production. Specific avenues of research needed to elaborate this framework will be outlined. [Work supported by NSERC.]
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".