The Use of Ultrasound in Remediation of North American English /r/ in 2 Adolescents
Bibliographic record
Abstract
PURPOSE: Ultrasound can provide images of the tongue during speech production. The present study set out to examine the potential utility of ultrasound in remediation of North American English /r/. METHOD: The participants were 2 Canadian English-speaking adolescents who had not yet acquired /r/. The study included an initial period without ultrasound and 13 treatment sessions, each 1 hr long, using ultrasound. Speech samples were recorded at screening and immediately before and after treatment. Samples were analyzed acoustically and with listener judgments. Ultrasound images were obtained before, during, and after the treatment period. RESULTS: Three speech-language pathologists unfamiliar with the participants rated significantly more posttreatment tokens as accurate [r]s in single words and some phrases. Acoustic analyses showed an expected lowering of the third formant after treatment. A qualitative observation of posttreatment ultrasound images for accurate [r] tokens showed tongue shapes to be more similar to those of typical adults than had been observed before treatment. Participants needed continued practice of their newly acquired skills in sentences and conversation. CONCLUSION: Two-dimensional dynamic ultrasound appears to have potential utility for remediation of /r/ in speakers with residual /r/ impairment. Further research is needed with larger numbers of participants to establish the relative efficacy of ultrasound in treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".