Application of Linear Discriminant Analysis to the Nasometric Assessment of Resonance Disorders: A Pilot Study
Bibliographic record
Abstract
Objective : Nasalance scores have traditionally been used to assess hypernasality. However, resonance disorders are often complex, and hypernasality and nasal obstruction may co-occur in patients with cleft palate. In this study, normal speakers simulated different resonance disorders, and linear discriminant analysis was used to create a tentative diagnostic formula based on nasalance scores for nonnasal and nasal speech stimuli. Materials and Methods : Eleven female participants were recorded with the Nasometer 6450 while reading nonnasal and nasal speech stimuli. Nasalance measurements were taken of their normal resonance and their simulations of hyponasal, hypernasal, and mixed resonance. Results : A repeated-measures analysis of variance revealed a resonance condition-stimuli interaction effect (P < .001). A linear discriminant analysis of the participants' nasalance scores led to formulas correctly classifying 64.4% of the resonance conditions. When the hyponasal and mixed resonance conditions with obstruction of the less patent nostril were removed from the analysis, the resultant formulas correctly classified 88.6% of the resonance conditions. Conclusion : The simulations produced distinctive nasalance scores, enabling the creation of formulas that predicted resonance condition above chance level. The preliminary results demonstrate the potential of this approach for the diagnosis of resonance disorders.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".