Automatic Acoustics Measurement of Audible Inspirations in Pathological Voices
Bibliographic record
Abstract
Audible inspiration (AI) is a type of speech perturbation commonly heard in pathologic voices. This acoustic parameter is used in conjunction with other acoustic observations to assess different types of pathologic conditions of speech associated with neurological or vocal cord disorders. However, the perception of this speech perturbation is often very subjective and difficult to appraise in a quantitative and consistent form. This work reports an algorithm to estimate the severity of this perturbation using time-frequency characteristics. The algorithm is based on a linear combination of the frequency of occurrence, the duration and the intensity of the inspirations. An algorithm to segment the AIs in conversational speech is proposed. The AI index was first evaluated with the Massachusetts Eye and Ear Infirmary Voice Database and then with two other databases containing recording from motor speech disorders. The segmentation algorithms was 90% effective detecting the audible inspirations and the AI index showed a high correlation (80.8%) with respect to the average perceptual judgment obtained from three judges with experience evaluating disordered speech.
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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 | 0.001 |
| 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 | 0.001 |
| 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".