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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".