Detection of voice disorders based on wavelet and prosody-related properties
Bibliographic record
Abstract
This paper presents an approach to detect voice disorders based on wavelet and prosody-related voice properties. First, several statistical measures of the normalized energy contents of the Discrete Wavelet Transform (DWT) coefficients over all voice frames are determined. Then, similar statistical measures of some prosody-related voice properties, such as mean pitch, jitter and shimmer are also computed over all the frames. In order to form a feature vector to be used in both training and testing phases, a set of statistical measure of the normalized energy contents of the DWT coefficients is combined with a set of statistical measure of the extracted prosody-related voice properties. Here, the voice samples under consideration are assumed to be of two categories, namely healthy and disordered thus formulating the problem in the proposed method as a two-class problem to be solved. Finally, the feature vector as obtained above is fed to an Euclidean Distance based classifier to detect the disordered voice. By performing extensive simulations, it is shown that the statistical analysis based on wavelet and prosody-related properties are able to provide effective detection of a variety of voice disorders from the mixture of healthy and disordered voices.
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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 | 0.000 | 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 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".