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Record W2331222116 · doi:10.3109/02699206.2016.1140227

Perceptual, durational and tongue displacement measures following articulation therapy for rhotic sound errors

2016· article· en· W2331222116 on OpenAlexafffund
Tim Bressmann, Susan Harper, Irina Zhylich, Gajanan V. Kulkarni

Bibliographic record

VenueClinical Linguistics & Phonetics · 2016
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
FundersInstitute of Human Development, Child and Youth HealthCanadian Institutes of Health Research
KeywordsArticulation (sociology)TonguePsychologyAudiologySession (web analytics)PerceptionDisplacement (psychology)Manner of articulationBiofeedbackMedicineComputer science

Abstract

fetched live from OpenAlex

Outcomes of articulation therapy for rhotic errors are usually assessed perceptually. However, our understanding of associated changes of tongue movement is limited. This study described perceptual, durational and tongue displacement changes over 10 sessions of articulation therapy for /ɹ/ in six children. Four of the participants also received ultrasound biofeedback of their tongue shape. Speech and tongue movement were recorded pre-therapy, after 5 sessions, in the final session and at a one month follow-up. Perceptually, listeners perceived improvement and classified more productions as /ɹ/ in the final and follow-up assessments. The durations of VɹV syllables at the midway point of the therapy were longer. Cumulative tongue displacement increased in the final session. The average standard deviation was significantly higher in the middle and final assessments. The duration and tongue displacement measures illustrated how articulation therapy affected tongue movement and may be useful for outcomes research about articulation therapy.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.403
Teacher spread0.270 · 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

Citations21
Published2016
Admission routes2
Has abstractyes

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