Comparison of Nasalance Scores Obtained with the Nasometer, the NasalView, and the OroNasal System
Bibliographic record
Abstract
OBJECTIVE: To compare nasalance scores obtained with the Nasometer, the NasalView, and the OroNasal System; evaluate test-retest reliability of the three systems; and explore whether three common text passages used for nasalance analysis could be shortened to a sentence each. SUBJECTS: Seventy-six adults with normal speech and hearing (mean age 26.5 years). PROCEDURES: Subjects read the complete Zoo Passage, Rainbow Passage, and Nasal Sentences. MAIN OUTCOME MEASURES: Mean nasalance magnitudes and mean nasalance distances were obtained with the three devices. RESULTS: The Nasometer had the lowest nasalance scores for the nonnasal Zoo Passage. The NasalView had the highest nasalance scores for the phonetically balanced Rainbow Passage. The OroNasal System had the lowest nasalance scores for the Nasal Sentences. The nasalance distance was largest for the Nasometer and smallest for the OroNasal System. Over 90% of the recordings were within 4% to 6% nasalance for most materials recorded with the Nasometer and the NasalView and within 7% to 9% for materials recorded with the OroNasal System. There were significant differences between the complete Zoo Passage and the Nasal Sentences and the individual sentences from these passages for the Nasometer and the OroNasal System. CONCLUSIONS: The three systems measure nasalance in different ways and provide nasalance scores that are not interchangeable. Test-retest variability for the Nasometer and the NasalView may be higher than previously reported. Individual sentences from the Zoo Passage and the Nasal Sentences do not provide nasalance scores that are equivalent to the complete passages.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".