Multi Parametric Method for the Objective Acoustic Evaluation of the Voice Produced by Laryngectomy Patients
Bibliographic record
Abstract
A reliable perceptive analysis implying several sessions of listening turns out to be consuming in time and in human resources, and therefore not allowing a regular use in clinical practice. At the level of the Ear, Nose and Throat (ENT) services the rehabilitative speech therapist is in charge of estimating by listening to the quality of the voice, which entraines a non reliable perceptive evaluation compared with the one that will be proposed in this work. In this study, objective instrumental approaches based on physical measures has been proposed. These approaches allowing the analysis and estimation of the acoustic characteristics of the voice of the patients having a total laryngectomy. The results are compared with those obtained in a healthy vocal speaker. The outcomes indicate that objective evaluations can provide a homogeneous database for a better interpretation of articulators and vocal dysfunctions.
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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 | 0.005 |
| 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 | 0.000 | 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".