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Record W1597112767

Speech compensation in persons who stutter: Acoustic and perceptual data

2011· article· en· W1597112767 on OpenAlexaffvenue
Aravind Kumar Namasivayam, Pascal van Lieshout

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsUniversity of TorontoThe Speech and Stuttering Institute
Fundersnot available
KeywordsSpeech recognitionVowelNeurocomputational speech processingCompensation (psychology)PerceptionComputer scienceMotor theory of speech perceptionSpeech productionAudiologyPsychologySpeech perceptionMedicineNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

A study examining speech compensation in persons who stutter is presented. The underlying assumption is that if PWS are at the lower end of a speech motor skill continuum one should be able to find differences between PWS and PNS in tasks which tax their abilities to control their speech motor system. If delayed compensation is found in PNS with perfectly intact sensory-motor systems. Then one would expect that if PWS have speech motor skill limitations, they would compensate to a lesser degree and/or may take longer time to adapt to oral articulatory perturbations relative to PNS. Word and vowel duration measures provide an index of how task requirements relating to speech rate are implemented by PWS and PNS. The data also revealed that PWS may require additional time compared to PNS to adapt to perturbations.

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)0.003
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.136
GPT teacher head0.332
Teacher spread0.196 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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