Articulation and Empirical Mode Decomposition Features in Diadochokinetic Exercises for the Speech Assessment of Parkinson's Disease Patients
Bibliographic record
Abstract
Speech impairments are one of the earliest manifestations in patients with Parkinson's disease. Particularly, articulation impairments related to the capability of the speaker to move the limbs and muscles of the vocal tract have been observed in the patients. Articulation deficits have been evaluated in the patients mainly using diadochokinetic exercises, which consist in the rapid repetition of syllables like /pa-ta-ka/. This study considered different features to model several aspects of the diadochokinetic exercises, including the capacity to start/stop the vocal fold vibration, the speech rate, and the regularity of the diadochokinetic task. Articulation features are combined with others that result from an empirical mode decomposition procedure, which have been recently used to model dysphonia in Parkinson's patients. The features are used to classify Parkinson's patients and healthy speakers, and to predict the dysarthria severity of the participants according to a clinical scale. According to the results, articulation features are able to classify the presence of the disease with an accuracy up to 76%, and to predict the dysarthria level of the speakers with a Spearman's correlation of up to 0.68.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".