Sensorimotor maps and vowel development in English, Greek, and Korean: A cross-linguistic perceptual categorization study.
Bibliographic record
Abstract
Learning to speak involves learning the association between different articulatory maneuvers and their associated auditory-perceptual characteristics. These associations may change over the course of development as a consequence of the nonlinearities inherent in vocal-tract growth. A set of recent studies by Ménard and colleagues has examined these developmental changes by studying adults’ categorizations of synthesized vowels based on an articulatory model of the growing vocal tract. These have shown that vowels modeled on younger vocal tracts tend to be perceived as more front than those modeled on older vocal tracts. They have also shown language-specificity in the vowels that are identified in growing vocal tracts, with French listeners labeling fewer sounds as /u/ than English ones, presumably because the high-vowel space of French includes three vowels /i y u/, while English has only two. The current experiment expands on this by examining the perception of synthesized vowels based on seven vocal-tract growth stages by speakers of three languages with very different vowel systems, American English, Korean (seven vowels), and modern Greek (five vowels). Preliminary analyses show language-specific patterns in the perception of vowels in developing vocal tracts. [work supported by NIDCD 02932 and NSF grants BCS0729140 and BCS0729277]
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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 | 0.000 | 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".