Enhancing Intervention for Residual Rhotic Errors Via App-Delivered Biofeedback: A Case Study
Bibliographic record
Abstract
Purpose: Recent research suggests that visual-acoustic biofeedback can be an effective treatment for residual speech errors, but adoption remains limited due to barriers including high cost and lack of familiarity with the technology. This case study reports results from the first participant to complete a course of visual-acoustic biofeedback using a not-for-profit iOS app, Speech Therapist's App for /r/ Treatment. Method: App-based biofeedback treatment for rhotic misarticulation was provided in weekly 30-min sessions for 20 weeks. Within-treatment progress was documented using clinician perceptual ratings and acoustic measures. Generalization gains were assessed using acoustic measures of word probes elicited during baseline, treatment, and maintenance sessions. Results: Both clinician ratings and acoustic measures indicated that the participant significantly improved her rhotic production accuracy in trials elicited during treatment sessions. However, these gains did not transfer to generalization probes. Conclusions: This study provides a proof-of-concept demonstration that app-based biofeedback is a viable alternative to costlier dedicated systems. Generalization of gains to contexts without biofeedback remains a challenge that requires further study. App-delivered biofeedback could enable clinician-research partnerships that would strengthen the evidence base while providing enhanced treatment for children with residual rhotic errors. Supplemental Material: https://doi.org/10.23641/asha.5116318.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".