Speech Dynamics and Resilience of Speech Production: Can Typically Fluent Adults Adapt to Long-Latency Delayed Auditory Feedback?
Bibliographic record
Abstract
Purpose: Delayed auditory feedback (DAF) is a potent speech perturbation that can induce disfluencies and speech errors along with slowing speaking rate in typical speakers. Typical speakers vary considerably in their susceptibility to DAF. In this study, we tested if typical speakers can completely suppress DAF effects or show a gradual, incomplete adaptation to DAF. Understanding the degree and time course of adaptation has clinical relevance as auditory manipulations are emerging as treatment options for several disorders. Method: Thirty-eight speakers read a single passage under nonaltered auditory feedback followed by 10–12 repeated readings of a different passage under 250-ms DAF. Disfluency rate (DR), articulation rate (AR), and reading duration were measured for each reading. The analysis used parametric statistics and growth models to test for change over time. Results: Over repeated readings of the same passage under DAF, DR decreased and AR increased compared to the first DAF reading for each speaker. Ten speakers reached our criteria for suppression of DAF effects, while a larger number approached the adaptation criteria for one of the dependent variables. Conclusions: Extended exposure to DAF during repeated readings promotes significant adaptation, but most speakers did not fully suppress the influence of DAF. Incomplete adaptation is more consistent with a slow process of modifying speech production strategies rather than rapid cognitive adjustments.
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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 | 0.001 |
| 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".