The association between speaker-dependent formant space estimates and perceived vowel quality
Bibliographic record
Abstract
Differences in speaker-dependent formant space estimates is discussed in terms of differing FF-scaling estimates. In the experiment, listeners were first trained to report apparent speaker FF-scaling using the training method outlined in Barreda & Nearey. 25 native speakers of Canadian English from the University of Alberta were drawn from a participant pool in which undergraduate linguistics students take part in experiments in exchange for partial course credit. During the testing phase, listeners were presented with fully-randomized, isolated-vowel stimuli. 18% of the variance in reported FF-scaling is found, with F1 accounting for 67.8%, 10 accounting for 28.1%, and F3 accounting for only 0.2% of the explained variance. Results show that FF-scaling has a significant negative effect on vowel openness, showing that for a given vowel sound, when listeners reported a higher FF-scaling, they were less likely to hear an open vowel.
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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 | 0.002 | 0.023 |
| 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 | 0.001 | 0.000 |
| 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".