Deficit or Difference? Effects of Altered Auditory Feedback on Speech Fluency and Kinematic Variability in Adults Who Stutter
Bibliographic record
Abstract
Purpose The purpose of this study was to test whether adults who stutter (AWS) display a different range of sensitivity to delayed auditory feedback (DAF). Two experiments were conducted to assess the fluency of AWS under long-latency DAF and to test the effect of short-latency DAF on speech kinematic variability in AWS. Method In Experiment 1, 15 AWS performed a conversational speaking task under nonaltered auditory feedback and 250-ms DAF. The rates of stuttering-like disfluencies, other disfluencies, and speech errors and articulation rate were compared. In Experiment 2, 13 AWS and 15 adults who do not stutter (AWNS) read three utterances under four auditory feedback conditions: nonaltered auditory feedback, amplified auditory feedback, 25-ms DAF, and 50-ms DAF. Across-utterance kinematic variability (spatiotemporal index) and within-utterance variability (percent determinism and stability) were compared between groups. Results In Experiment 1, under 250-ms DAF, the rate of stuttering-like disfluencies and speech errors increased significantly, while articulation rate decreased significantly in AWS. In Experiment 2, AWS exhibited higher kinematic variability than AWNS across the feedback conditions. Under 25-ms DAF, the spatiotemporal index of AWS decreased significantly compared to the other feedback conditions. AWS showed lower overall percent determinism than AWNS, but their percent determinism increased under 50-ms DAF to approximate that of AWNS. Conclusions Auditory feedback manipulations can alter speech fluency and kinematic variability in AWS. Longer latency auditory feedback delays induce speech disruptions, while subtle auditory feedback manipulations potentially benefit speech motor control. Both AWS and AWNS are susceptible to auditory feedback during speech production, but AWS appear to exhibit a distinct continuum of sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".