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Record W2995082118 · doi:10.1044/2019_jslhr-19-00145

Early Speech Rate Development: A Longitudinal Study

2019· article· en· W2995082118 on OpenAlexaff
Anna Tendera, Matthew Rispoli, Ambikaipakan Senthilselvan, Torrey M. Loucks

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUtteranceMean length of utterancePsychologyLongitudinal studyLanguage developmentAudiologyMetric (unit)GrammarSpeech productionLinguisticsSpeech recognitionComputer scienceDevelopmental psychologyMathematicsMedicineStatistics

Abstract

fetched live from OpenAlex

Purpose Our knowledge of speech rate development remains inadequate because of limited longitudinal data and lack of data from children under age 3;0 (years;months). The purpose of this longitudinal study was to test the pattern of speech rate development between ages 2;0 and 3;0. Method Speech rate was assessed at 4 time points between ages 2;0 and 3;0. The analysis employed multilevel models to characterize the development of speech rate (syllables per second), phonemes per second (PPS), length of active declarative sentences, and mean length of utterance. Results The results indicate a significant linear increase in speech rate, PPS, length of active declarative sentences, and mean length of utterance occurring over the 1-year period. Male and female children differed in speech rate, PPS, and utterance length, suggesting sex is a potential factor in early speech rate development. Conclusions Our findings indicate that the speech motor system develops rapidly during the period when grammar emerges. Speech rate has the potential to be an important metric for understanding typical speech development and speech 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.089
GPT teacher head0.414
Teacher spread0.326 · 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.

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

Citations16
Published2019
Admission routes1
Has abstractyes

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