Are idiosyncrasies in vowel production free or learned? A study of variants of the French vowel system in biological brothers
Bibliographic record
Abstract
Speech production displays a number of idiosyncrasies that are individual variations in the way speakers achieve phonetic contrasts in their language. It was shown previously [Ménard L. et al., Speech Commun. 2008, 50(1), 14-28] that idiosyncrasies in the production of the height contrast in oral vowels in French are characterized by large variations in the distribution of F1 values, which are associated with a stability of F1 for a given height degree, independent of the place of articulation (front vs. back) and rounding. The current study aimed to assess whether these idiosyncrasies are random or induced by the learning environment. Ten pairs of French Canadian adult male siblings were recruited, and each individual was asked to produce ten repetitions of the ten French oral vowels, which were recorded. F1 values were extracted using linear predictive coding algorithms. There was a trend towards imposed variations, since the distances between F1 values for brothers for a given vowel were significantly smaller than the corresponding distances between speakers who were not brothers. However, the F1 values within pairs of brothers were significantly correlated for only two of the six mid-high or mid-low vowels. Thus, it appears that a large part of the idiosyncrasies in the pronunciation of vowels were random and differed between brothers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".