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Record W1979632996 · doi:10.1044/2014_jslhr-s-14-0077

Dialectical Effects on Nasalance: A Multicenter, Cross-Continental Study

2014· article· en· W1979632996 on OpenAlexaffabout
Shaheen N. Awan, Tim Bressmann, Bruce J. Poburka, Nelson Roy, Helen M. Sharp, Christopher R. Watts

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2014
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNasalityAudiologyPsychologyNorth westVariation (astronomy)GeographyHistoryLinguisticsMedicineVowelPhysical geography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.473
Teacher spread0.417 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2014
Admission routes2
Has abstractyes

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