Comparison of intelligibility measures for children with velopharyngeal insufficiency: visual analog scale ratings, interval scales, and orthographic transcription (OT)-based measures
Bibliographic record
Abstract
Regardless of the underlying cause for speech impairment in speakers with cleft palate, a universal consequence of cleft palate is reduced speech intelligibility. Still, there is no standardised approach for measuring intelligibility for speakers with cleft speech. The current study aimed to determine the relationship between orthographic transcription (OT)-based measures, interval-scale ratings, and visual analog scale (VAS) ratings for perceptual judgements of intelligibility in speakers with cleft palate as judged by speech-language pathologists (SLPs). The speaker participants were six speakers with velopharyngeal insufficiency secondary to cleft palate. Four sets of sentences from the Hearing in Noise Test were recorded from each speaker. A total of 14 SLPs provided their intelligibility judgement on these speaker’s recordings by word-by-word orthographic transcriptions, a visual analog scale (0–100), and a 5-point interval rating scale. A Spearman rank correlation test indicated a negative, strong correlation between OT-based measurements and VAS scores (r = −.94; p = 0.01) and between OT-based measurements and interval rating scores (r = −.77, p = 0.01). A strong, positive correlation was found between scores obtained from VAS and interval rating scales (r = .83, p = 0.05). The strong relationship between the objective measure of intelligibility (i.e. OT-based measure) and a subjective measure of intelligibility (i.e. VAS and interval scale) supports using a less time-consuming VAS as a substitute for orthographic transcription in measuring intelligibility in cleft palate speech.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".