Using auditory feedback during vocalization: Different mechanisms for utterance onset and pitch maintenance.
Bibliographic record
Abstract
During vocalization, auditory feedback is used to attain and maintain a desired fundamental frequency (F0). The use of auditory feedback to control vocalization has been studied using adaptation studies, in which an F0 feedback alteration is maintained across many trials, and using midutterance perturbations, where a brief F0 change is introduced during an ongoing utterance. Participants vocalized during two blocks. In an adaptation block, a one semitone F0 shift was maintained for 40 trials, and midutterance perturbations were introduced by removing the feedback alteration. In a control block, a midutterance perturbation was introduced in trials, which were either unshifted or shifted randomly by one semitone at before the onset of vocalization (similar to the adaptation trials). F0 compensation to midutterance perturbations was identical in all conditions, and was always smaller than the compensation to a shift at utterance onset. These results are explained by a change in the control strategy at utterance onset and mid-utterance. At utterance onset, auditory feedback is compared to feedback predicted by a forward model to ensure the pitch goal is achieved. However, after utterance onset, the control strategy switches and stabilization is maintained by comparing feedback to previous F0 production.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".