Normative Nasalance Scores for Middle-Aged and Elderly Speakers of Brazilian Portuguese
Bibliographic record
Abstract
OBJECTIVES: This study establishes normative nasalance values for middle-aged and elderly Brazilian Portuguese-speakers and investigates age and gender effects across the life span. METHODS: Nasalance scores were obtained from 62 middle-aged (45-59 years) and 60 elderly (60-79 years) participants with normal speech for 3 nonnasal, 1 phonetically balanced, and 2 nasal-loaded test sentences using the Nasometer II 6400. The data were combined with a published data set of 237 speakers in 4 groups: children (5-9 years), adolescents (10-19 years), young adults (20-24 years), and mature adults (25-35 years). A repeated-measures analysis of variance was used to investigate differences between the stimuli by gender and age groups. RESULTS: There were statistically significant effects of stimulus, gender, and age group, as well as a stimulus-age group interaction effect and a gender-age group interaction effect. The females' mean nasalance scores were higher than those of the males. The mean nasalance scores for the child, adolescent, and young and mature adult speakers were significantly lower than those for the elderly speakers, and the children's scores were significantly lower than those of the middle-aged speakers. CONCLUSION: Higher nasalance scores among middle-aged and elderly speakers may indicate physiological changes affecting oral-nasal balance in speech across the life span.
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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 | 0.003 |
| 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) | 0.002 | 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".