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Record W2561576097 · doi:10.1597/16-096

Production of two Nasal Sounds by Speakers with Cleft Palate

2016· article· en· W2561576097 on OpenAlexfundno aff
Tim Bressmann, Bojana Radovanović, Susan Harper, Paula Klaiman, David M. Fisher, Gajanan V. Kulkarni

Bibliographic record

VenueThe Cleft Palate-Craniofacial Journal · 2016
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsAudiologyVowelConsonantMedicineArticulation (sociology)Soft palateSpeech recognitionComputer science

Abstract

fetched live from OpenAlex

Many speakers with cleft palate develop atypical consonant productions, especially for pressure consonants such as plosives, fricatives, and affricates. The present study investigated the nature of nasal sound errors. The participants were eight female and three male speakers with cleft palate between the ages of 6 to 20. Speakers were audio-recorded, and midsagittal tongue movement was captured with ultrasound. The speakers repeated vowel-consonant-vowel with the vowels /α/, /i/, and /u/ and the alveolar and velar nasal consonants /n/ and /η/. The productions were reviewed by three listeners. The participants showed a variety of different placement errors and insertions of plosives, as well as liquid productions. There was considerable error variability between and within speakers, often related to the different vowel contexts. Three speakers co-produced click sounds. The study demonstrated the wide variety of sound errors that some speakers with cleft palate may demonstrate for nasal sounds. Nasal sounds, ideally in different vowel contexts, should be included in articulation screenings for speakers with cleft palate, perhaps more than is currently the case.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.309
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Cleft Palate-Craniofacial JournalSame topicPhonetics and Phonology ResearchFrench-language works237,207