Does the production of speech necessarily rely on auditory feedback?
Bibliographic record
Abstract
Talkers show sensitivity to a range of perturbations of auditory feedback (e.g., manipulation of vocal amplitude, fundamental frequency, formant frequency). When the auditory concomitants of speech (feedback) are perturbed, talkers compensate in order to preserve accurate productions. These effects demonstrate the role of sensory feedback in articulatory control and have been used to study the role of prediction in motor planning. In this study, 15 subjects spoke a monosyllable (‘head’) and the formants in their utterances were shifted in real time using a custom signal processing system and were fed back over headphones with an imperceptible delay. The first and second formants were altered so that the auditory feedback matched the subjects’ productions of ‘had’. Despite explicit instruction to ignore the feedback changes and maintain consistent production of ‘head’, subjects produced a robust compensation similar to the compensatory behavior shown by naive subjects. 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 automatic and obligatory. Results will be discussed in terms of the representations that guide speech motor control. Work supported by NIDCD and NSERC.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".