Speech Production in Healthy Older Adults With or Without Amateur Singing Experience
Bibliographic record
Abstract
PURPOSE: Amateur singing is a universal, accessible, and enjoyable musical activity that may have positive impacts on human communication. However, evidence of an impact of singing on speech articulation is still scarce, yet understanding the effects of vocal training on speech production could provide a model for treating people with speech deficits. The aim of this study was to examine speech production in younger and older adults with or without amateur singing experience. METHOD: Thirty-eight amateur singers (aged 20-87 years, 23 women and 15 men) and 40 nonmusician active controls (aged 23-88 years, 19 women and 21 men) were recruited. A set of tasks were used to evaluate the oral motor sphere: two voice production tasks, a passage reading task, and a modified diadochokinetic (DDK) rates task performed at a natural rhythm and as quickly as possible. RESULTS: Our results show that older age was associated with lower reading rate, lower articulation rate, and articulation rate variability in the DDK task, as well as reduced accuracy for the phonologically complex stimuli. Most importantly, our results show an advantage for singers over cognitively active nonsingers in terms of articulatory accuracy in the most challenging situations. CONCLUSION: This result suggests extended maximal performance capacities in amateur singers perhaps resulting from the articulatory efforts required during singing. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24274813.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".