Dialectical Effects on Nasalance: A Multicenter, Cross-Continental Study
Bibliographic record
Abstract
PURPOSE: This study investigated nasalance in speakers from six different dialectal regions across North America using recent versions of the Nasometer. It was hypothesized that many of the sound changes observed in regional dialects of North American English would have a significant impact on measures of nasalance. METHOD: Samples of the Zoo Passage, the Rainbow Passage, and the Nasal Sentences were collected from young adult male and female speakers (N=300) from six North American dialectical regions (Midland/Mid-Atlantic; Inland North Canada; Inland North; North Central; South; and Western dialects). RESULTS: Across the three passage types, effect sizes for dialect were moderate in strength and accounted for approximately 7%-9% of the variation in nasalance. Increased differences in nasalance tended to occur between speakers from distinctly different geographical regions, with the highest nasalance across all passages observed for speakers from the Texas South dialect region. CONCLUSION: Clinicians and researchers who use perceptual and instrumental measures of speech production should be aware that dialectical and socially acquired speech patterns may influence the acoustic characteristics of speech and may also influence the interpretation of normative expectations and typical versus disordered cutoff scores for instruments such as the Nasometer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".