Acoustic prominence of verbal instructions in Parkinson's disease
Bibliographic record
Abstract
Talkers signal important information to listeners in part by manipulating prosodic prominence. In English, this is typically achieved by producing more prominent words with greater durations, intensity, f0, and hyperarticulation, though this tendency may be reduced in talkers with motor speech disorders. In the present study, older adults with and without Parkinson’s disease (PD) and dysarthria engaged in a simple instructional game activity in which they directed a research confederate to move around pictures on a board. Utterances were designed to manipulate prominence on stop-initial monosyllabic target words that differed in onset voicing and following vowel quality. Non-prominent words were repeated in the spoken instructions (“Move the pot above the chair, now move the [pot] above the box”), while prominent words contrasted with a minimal pair differing in onset voicing (“Move the pot above the chair, now move the [bot] above the chair”). Eleven people with PD and 11 age- and gender-matched controls participated. Overall, prominent words were longer, louder, and higher pitched, though control speakers modulated f0 to a greater extent than talkers with PD. Both groups modulated the amount of voicing during closure but not VOT as a means of increasing stop voicing contrasts in prominent words.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".