Enhancing speech adaptation to a palatal perturbation using ultrasound visual biofeedback
Bibliographic record
Abstract
Prior clinical studies suggest that visual biofeedback of the tongue (e.g., ultrasound) may enhance the treatment of speech disorders, but outcomes have been mixed, likely due to variability in both the clinical profiles of participants and the way in which treatments have been carried out. Understanding the true potential of this clinical tool requires a clearer sense of how visual biofeedback interacts with speech motor control. Here, we present a novel experimental approach that mimics the conditions of clinical treatment for speech disorders while maintaining a high level of consistency and control over the speech-learning task. The procedure involves altering the vocal tract of typically developing talkers using a palatal prosthesis to perturb /s/-production in combination with the controlled application of ultrasound biofeedback. As participants practice and improve their speech, changes in articulatory movements are examined using acoustic and kinematic measures. The present study compared a control group (n = 10) receiving only auditory feedback during speech practice with a group receiving visual biofeedback (n = 10). Results indicate an effect of biofeedback, in particular in the retention of learned motor patterns, indicating that talkers integrate real-time visual feedback of tongue movement into the sensorimotor processes driving speech adaptation.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".