Understanding speaker attitudes from prosody by adults with Parkinson's disease
Bibliographic record
Abstract
The ability to interpret vocal (prosodic) cues during social interactions can be disrupted by Parkinson's disease, with notable effects on how emotions are understood from speech. This study investigated whether PD patients who have emotional prosody deficits exhibit further difficulties decoding the attitude of a speaker from prosody. Vocally inflected but semantically nonsensical 'pseudo-utterances' were presented to listener groups with and without PD in two separate rating tasks. Task I required participants to rate how confident a speaker sounded from their voice and Task 2 required listeners to rate how polite the speaker sounded for a comparable set of pseudo-utterances. The results showed that PD patients were significantly less able than HC participants to use prosodic cues to differentiate intended levels of speaker confidence in speech, although the patients could accurately detect the politelimpolite attitude of the speaker from prosody in most cases. Our data suggest that many PD patients fail to use vocal cues to effectively infer a speaker's emotions as well as certain attitudes in speech such as confidence, consistent with the idea that the basal ganglia play a role in the meaningful processing of prosodic sequences in spoken language (Pell & Leonard, 2003).
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".