Discrimination of pathological voices using an adaptive time-frequency approach
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Bibliographic record
Abstract
Acoustic measures of vocal function are routinely used for the assessment of disordered voice, and for monitoring patient's progress over the course of therapy. In current clinical practice, acoustic measures extracted from sustained vowels are used for vocal function characterization. However, the measures derived from continuous speech samples are required for accurate assessment of voice quality. In this paper, a time-frequency approach for pathological voice discrimination has been proposed. The speech signals were decomposed using an adaptive time-frequency transform algorithm, and the signal decomposition parameters such as the octave (scale) maximum, octave mean, energy rate, and length ratio were analyzed using the maximum likelihood method and Jack-knife algorithm for classification. A classification accuracy of 90% was obtained with a database of 40 speech signals (20 normal and 20 pathological cases).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 it