Listener impressions of speakers with Parkinson’s disease
Bibliographic record
Abstract
Parkinson's disease (PD) has several negative effects on speech production and communication. However, few studies have looked at how speech patterns in PD contribute to linguistic and social impressions formed about PD patients from the perspective of listeners. In this study, discourse recordings elicited from nondemented PD speakers (n = 18) and healthy controls (n = 17) were presented to 30 listeners unaware of the speakers' disease status. In separate conditions, listeners rated the discourse samples based on their impressions of the speaker or of the linguistic content. Acoustic measures of the speech samples were analyzed for comparison with listeners' perceptual ratings. Results showed that although listeners rated the content of Parkinsonian discourse as linguistically appropriate (e.g., coherent, well-organized, easy to follow), the PD speakers were perceived as significantly less interested, less involved, less happy, and less friendly than healthy speakers. Negative social impressions demonstrated a relationship to changes in vocal intensity (loudness) and temporal characteristics (dysfluencies) of Parkinsonian speech. Our findings emphasize important psychosocial ramifications of PD that are likely to limit opportunities for communication and social interaction for those affected, because of the negative impressions drawn by listeners based on their speaking voice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".