Asymmetries in vowel perception align with focalization patterns, not peripherality: Acoustic evidence from Canadian French /e/ and /Ø/
Bibliographic record
Abstract
The question of whether asymmetries in vowel perception stem from peripherality (location in F1/F2 vowel space) or focalization patterns (formant convergence) was examined. Prior studies with Danish and Dutch listeners reported asymmetric perception of /e-Ø/ such that discriminating a change from /e/ to /Ø/ was easier compared to the reverse direction (/Ø/ to /e/). Acoustic analyses of test stimuli suggest that focalization patterns predict this asymmetry whereas a peripherality-based prediction fails. Although this interpretation is compelling, it is based on analyses of several vowel tokens produced by a single talker. To confirm the reliability of these patterns we obtained acoustic and electromagnetic articulography recordings of Canadian French /e/ and /Ø/ productions from a broader sample of talkers (n = 20): 400 vowels/10 productions per vowel. Acoustic analyses revealed a robust pattern for all talkers: F1 and F2 (also F1 and F3) were spectrally closer for /Ø/ compared to /e/, and /e/ was also more peripheral than /Ø/ in F1/F2 space. These data align with the earlier (single-talker) analyses and confirm that /Ø/ is a more focal vowel than /e/. We will also discuss articulatory analyses of these French vowel productions that focus on how vocal-tract constrictions relate to focalization and peripherality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".