Auditory feedback and articulatory timing.
Bibliographic record
Abstract
Talkers listen to their own voice while they speak and use that feedback to monitor and control fine details of speech production. When auditory feedback is perturbed in real time, talkers spontaneously alter their speech production to compensate for the perturbation. Most research using real-time altered auditory feedback has focused on spectral manipulations of vowels with little attention devoted to temporal manipulations of consonants. In the present study, we examine the role of acoustic feedback in control of voice onset time (VOT). Utterances of the words “tip” and “dip” were recorded from native English speakers, and several representative productions were selected for each speaker. After this, talkers were asked to repeatedly produce either tip or dip. During these productions a real-time processing system was used to provide modified feedback through headphones. When talkers said one word, they simultaneously heard their own voice saying the other word. Results showed that the speakers compensated for the VOT perturbation such that they lengthened their VOT for /t/ when the VOT of the feedback was shorter (/d/). Based on these results, a comparison of the role of auditory feedback in controlling temporal and spectral aspects of speech production will be discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".