Normative Nasalance Scores for Brazilian Portuguese Using New Speech Stimuli
Bibliographic record
Abstract
OBJECTIVE: Normative data were established for newly developed speech materials for nasalance assessment in Brazilian Portuguese. MATERIALS AND METHODS: Nasalance scores of preexisting passages (oral ZOO-BR, low-pressure oral ZOO-BR2 and NASAL-BR), new nasalance passages (oral Dudu no zoológico, oral Dudu no bosque, oral-nasal O cãozinho Totó and nasal O nenê) and Brasilcleft articulation screening sentences were collected from 245 speakers of Brazilian Portuguese, including 121 males and 124 females, divided into 4 groups: children (5-9 years), adolescents (10-19 years), young adults (20-24 years) and adults (25-35 years). RESULTS: Across all nasalance passages, adult females scored on average 2 percentage points higher than males. Children scored 2-4 percentage points lower than older groups for the preexisting nasalance passages ZOO-BR and ZOO-BR2. Nasalance scores for the new nasalance passages were not significantly different from the preexisting passages. Scores for high-pressure sentences did not differ significantly from the oral nasalance passage Dudu no bosque. CONCLUSION: The nasalance scores for the new nasalance passages were equivalent to the preexisting materials. The new shortened and simplified nasalance passages will be useful for assessing young children. Normative scores for the Brasilcleft high-pressure sentences were equivalent to the new oral passage Dudu no bosque.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".