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Record W2120532526 · doi:10.1121/1.4782271

A motor differentiation model for liquid substitutions in children’s speech

2007· article· en· W2120532526 on OpenAlexaff
Bryan Gick

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpeech productionProduction (economics)Task (project management)Computer scienceLinguisticsSpeech errorPsychologyAudiologySpeech recognitionMedicinePhilosophy

Abstract

fetched live from OpenAlex

Studies of lip-jaw coordination in children have shown a lack of motor differentiation between anatomically coupled articulators in young childrens speech [Green & al. 2000, JSLHR 43: 239–255]. A model is described in which children contending with their developing motor systems generally strive to reduce the degrees of freedom of complex anatomical structures (e.g., the tongue). The claim is pursued that segmental substitutions (e.g., /w/ replacing /r/ or /l/) are the result of specific compensation strategies which aim to simplify the complexity of the articulatory task. The proposal that gestural simplification may dictate substitution strategies for liquid consonants has been suggested previously [Studdert-Kennedy & Goldstein 2003, Language Evolution, Oxford U. Pr. 235-254]. It is proposed here that gestural simplification may be achieved via one of two basic mechanisms: gestural omission and stiffening (and hence merger), and that these two mechanisms account for all of the commonly attested substitutions for English /r/ and /l/. Supporting data are presented from ultrasound studies of: postvocalic /r/ production of an 11-month-old female speaker of English, liquid production of a group of 3–5-year-old speakers of English, and liquid production and substitutions in the speech of adolescent speakers of English with speech and hearing disorders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.026
GPT teacher head0.331
Teacher spread0.305 · 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 designSimulation or modeling
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

Citations26
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicPhonetics and Phonology ResearchFrench-language works237,207