Singing and Speaking Ability in Parkinson's Disease and Spinocerebellar Ataxia
Bibliographic record
Abstract
PURPOSE: This study examined spontaneous, spoken-to-a-model, and two sung modes in speakers with Parkinson's disease (PD), speakers with cerebellar disease (CD), and healthy controls. Vocal performance was measured by intelligibility scores and listeners' perceptual ratings. METHOD: Participants included speakers with hypokinetic dysarthria secondary to PD, those with ataxic dysarthria secondary to CD, and healthy speakers. Participants produced utterances in four vocal modes: spontaneous speech, spoken-to-a-model, sung-to-a-model, and spontaneous singing. For spoken-to-a-model and sung-to-a-model modes, written material was provided the model. For spontaneous singing, participants sang songs that they endorsed as familiar. DEPENDENT VARIABLES: In Experiment I, listeners orthographically transcribed the audio samples of the first three vocal modes. In Experiment IIa, raters evaluated the accuracy of the pitch and rhythm of the spontaneous singing of familiar songs. Finally, familiar songs and sung-to-a-model utterances were rated on a competency scale by a second group of raters (Experiment IIb). RESULTS: Results showed increases in intelligibility during the spoken-to-a-model mode compared with the spontaneous mode in both PD and CD groups. Singing enhanced the vocal output of speakers with PD more than in speakers with CD, as measured by percent intelligibility. PD participants' pitch and rhythm accuracy and competency in singing familiar songs was rated more favorably than those produced by CD participants. CONCLUSIONS: The findings reveal a vocal task effect for spoken utterances in both groups. Sung exemplars, more impaired in CD, suggest a significant involvement of the cerebellum in singing. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21809544.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".