Application of Linear Discriminant Analysis to the Long-term Averaged Spectra of Simulated Disorders of Oral-Nasal Balance
Bibliographic record
Abstract
OBJECTIVE: Acoustic studies of oral-nasal balance disorders to date have focused on hypernasality. However, in patients with cleft palate, nasal obstruction may also be present, so that hypernasality and hyponasality co-occur. In this study, normal speakers simulated different disorders of oral-nasal balance. Linear discriminant analysis was used to create a tentative diagnostic formula based on the long-term averaged spectra (LTAS) of the speech stimuli. MATERIALS AND METHODS: Eleven female participants were recorded while reading nonnasal and nasal speech stimuli. LTASs of the recordings were run for their normal oral-nasal balance and their simulations of hyponasal, hypernasal, and mixed oral-nasal balance. The amplitude values (in decibels) were extracted in 100-Hz intervals over a range of 4 kHz. RESULTS: A repeated-measures analysis of variance of the normalized amplitudes revealed a resonance condition-frequency band amplitude interaction effect (P < .001). A linear discriminant analysis of the participants' LTAS led to formulas correctly classifying 80.7% of the oral-nasal balance conditions. CONCLUSION: The simulations produced distinctive spectra enabling the creation of formulas that predicted the oral-nasal balance above chance level. Future research with speakers with oral-nasal balance disorders will be needed to investigate the potential of this approach for the clinical diagnosis of disorders of oral-nasal balance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".