Stabilizing variability in the auditory feedback of speech
Bibliographic record
Abstract
Auditory feedback is an essential part of speech motor control and speech learning. When feedback is perturbed in laboratory settings (e.g., Houde and Jourdan, 1998), speakers, on average, compensate for the perceived error. There is, however, considerable individual variability observed in natural speech and in speakers’ responses to auditory feedback manipulations (e.g., Purcell and Munhall, 2006). Here, we introduce a novel manipulation that stabilized the predictability of auditory feedback of 20 female speakers. Participants produced the English word “head” 95 times in two different conditions. In the Control condition, subjects produced all utterances with unaltered auditory feedback. In the Stabilization condition, subjects were presented with a recording of one of their own utterances of “head” synchronized with their speech on some trials. Auditory feedback was thus made constant by playing the same recording for a set of 30 trials. Trial-to-trial variability of the talkers’ speech did not change as a result of this constant feedback. Time-series analyses were performed to examine whether production variability differed among speakers in the two conditions. Results will be discussed regarding the possible role of variability in speech motor control and the importance of developing methods to detect state-change in individual time-series data.
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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 | 0.005 |
| 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) | 0.001 | 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".