Perturbing the consistency of auditory feedback in speech
Bibliographic record
Abstract
Sensory information, including auditory feedback, is used by talkers to maintain fluent speech articulation. Current models of speech motor control posit that speakers continually adjust their motor commands based on discrepancies between the sensory predictions made by a forward model and the sensory consequences of their speech movements. Here, in two within-subject design experiments, we used a real-time formant manipulation system to explore how reliant speech articulation is on the accuracy or predictability of auditory feedback information. This involved introducing random formant perturbations during vowel production that varied systematically in their spatial location in formant space (Experiment 1) and temporal consistency (Experiment 2). Our results indicate that, on average, speakers' responses to auditory feedback manipulations varied based on the relevance and degree of the error that was introduced in the various feedback conditions. In Experiment 1, speakers' average production was not reliably influenced by random perturbations that were introduced every utterance to the first (F1) and second (F2) formants in various locations of formant space that had an overall average of 0 Hz. However, when perturbations were applied that had a mean of +100 Hz in F1 and -125 Hz in F2, speakers demonstrated reliable compensatory responses that reflected the average magnitude of the applied perturbations. In Experiment 2, speakers did not significantly compensate for perturbations of varying magnitudes that were held constant for one and three trials at a time. Speakers' average productions did, however, significantly deviate from a control condition when perturbations were held constant for six trials. Within the context of these conditions, our findings provide evidence that the control of speech movements is, at least in part, dependent upon the reliability and stability of the sensory information that it receives over time.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".