Automatic Assessment of Perturbations Produced by Audible Inspirations in Pathological Voices
Bibliographic record
Abstract
Audible inspiration is a type of speech perturbation used in conjunction with other acoustic observations to assess different types of pathologic conditions of speech associated with neurological or vocal cord disorders. The perception of this voice perturbation is very subjective and difficult to appraise in a consistent form across multiple utterances, subjects and disorders. This work reports an algorithm to model the perception of audible inspirations. It automatically segments the inspirations in continuous speech based on time-frequency characteristics and estimates the magnitude of the perturbation through a linear combination of the number, duration and the intensity of the inspirations. The segmentation algorithm was evaluated with the Massachusetts Eye and Ear Infirmary Voice database and two other databases containing recording from motor speech disorders. Results: a new method to automatically segment inspiratory phonations was developed in addition to effective multi-variable models of the perception of inspirations. An average segmentation accuracy of 84.4% was achieved enabling accurate objective judgments of the perturbations associated with audible inspirations (80.9% correlation).
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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 | 0.002 |
| 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".