Talkers alter vowel production in response to real-time formant perturbation even when instructed not to compensate
Bibliographic record
Abstract
Talkers show sensitivity to a range of perturbations of auditory feedback (e.g., manipulation of vocal amplitude, fundamental frequency and formant frequency). Here, 50 subjects spoke a monosyllable ("head"), and the formants in their speech were shifted in real time using a custom signal processing system that provided feedback over headphones. First and second formants were altered so that the auditory feedback matched subjects' production of "had." Three different instructions were tested: (1) control, in which subjects were naive about the feedback manipulation, (2) ignore headphones, in which subjects were told that their voice might sound different and to ignore what they heard in the headphones, and (3) avoid compensation, in which subjects were informed in detail about the manipulation and were told not to compensate. Despite explicit instruction to ignore the feedback changes, subjects produced a robust compensation in all conditions. There were no differences in the magnitudes of the first or second formant changes between groups. In general, subjects altered their vowel formant values in a direction opposite to the perturbation, as if to cancel its effects. These results suggest that compensation in the face of formant perturbation is relatively automatic, and the response is not easily modified by conscious strategy.
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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 | 0.001 |
| 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) | 0.003 | 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".