Perceptual, acoustic, and tongue shape measures during /r/ production pre- and post-treatment using visual feedback from ultrasound: case studies of two adolescents
Bibliographic record
Abstract
This study examined the effectiveness of using visual feedback from ultrasound in remediation of persistent /r/ errors. Ultrasound provided the participants and the clinician with a dynamic sagittal or coronal image of the tongue during speech production. The participants in this study were two adolescent boys ages 12 and 14 who were not able to produce an on-target North American /r/. Both participants had received at least one year of traditional /r/ therapy without improvement. Treatment was provided over 13 one-hour sessions using visual feedback from ultrasound. Initially, /r/ was broken down and rehearsed as individual motor targets (tongue tip, body, root); these components were then practiced in combination to produce /r/ in isolation, then in syllables, words, and phrases. Post-treatment changes in /r/ production were captured through transcription, acoustic analysis, and tongue shape measurement. Both participants /r/ productions were rated as having more tokens of on-target /r/ post-treatment. Acoustic results supported these findings with the third formant lowering post-treatment. Tongue shape measures indicated that the participants tongue shapes were more similar to the modeled /r/ tongue shape post-treatment. These case studies suggest that visual feedback as provided by ultrasound may be a useful adjunct to speech (re)habilitation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".