Compensations in response to real-time formant perturbations of different magnitudes
Bibliographic record
Abstract
Previous auditory perturbation studies have demonstrated that talkers spontaneously compensate for real-time formant-shifts by altering formant production in a manner opposite to the perturbation. Here, two experiments were conducted to examine the effect of amplitude of perturbation on the compensatory behavior for the vowel /epsilon/. In the first experiment, 20 male talkers received three step-changes in acoustic feedback: F1 was increased by 50, 100, and 200 Hz, while F2 was simultaneously decreased by 75, 125, and 250 Hz. In the second experiment, 21 male talkers received acoustic feedback in which the shifts in F1 and F2 were incremented by +4 and -5 Hz on each utterance to a maximum of +350 and -450 Hz, respectively. In both experiments, talkers altered production of F1 and F2 in a manner opposite to that of the formant-shift perturbation. Compensation was approximately 25%-30% of the perturbation magnitude for shifts in F1 and F2 up to 200 and 250 Hz, respectively. As larger shifts were applied, compensation reached a plateau and then decreased. The similarity of results across experiments suggests that the compensatory response is dependent on the perturbation magnitude but not on the rate at which the perturbation is introduced.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".