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Record W4410034509 · doi:10.3390/bs15050605

English-Learning Infants’ Developing Sound System Guides Their Early Word Learning

2025· review· en· W4410034509 on OpenAlexafffund
Suzanne Curtin, Susan A. Graham

Bibliographic record

VenueBehavioral Sciences · 2025
Typereview
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of CalgaryBrock University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCurtin University of Technology
KeywordsLanguage acquisitionSound (geography)Computer scienceWord (group theory)Process (computing)Speech soundSpeech recognitionWord learningLinguisticsPsychologyAcousticsVocabulary

Abstract

fetched live from OpenAlex

Children appear to acquire new words effortlessly from complex auditory input. However, this process is highly intricate, requiring the simultaneous integration of phonetic and phonemic details, prosodic cues, and grammatical structures. Furthermore, different components of a language's sound system-such as phonemes, syllables, and prosodic features-appear with different frequencies in the input and follow distinct patterns of distribution in speech. This article reviews research that illustrates how infants' growing understanding of their native language sound system facilitates their acquisition of new words.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.105
GPT teacher head0.409
Teacher spread0.304 · 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 designOther design
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes2
Has abstractyes

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