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Additive Effects of Lengthening on the Utterance-Final Word in Child-Directed Speech

2012· article· en· W2024350409 on OpenAlexafffund
Eon-Suk Ko, Mélanie Söderström

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2012
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUtteranceSentenceRegister (sociolinguistics)Focus (optics)Word (group theory)Duration (music)Statement (logic)LinguisticsPsychologySpeech recognitionComputer scienceNatural language processing

Abstract

fetched live from OpenAlex

PURPOSE: The authors investigated lengthening effects in child-directed speech (CDS) across the sentence, testing the additive effects on duration of Word Position, Register, Focus, and Sentence Mode (statement/question). METHOD: Five theater students produced 6 sentences containing 5 monosyllabic words in a simulated dialogue, varying in Register, Focus, and Sentence Mode. The authors segmented a total of 1,800 sentences using forced-alignment tools, and they analyzed the duration of each word. RESULTS: The results show significant effects of Register, Word Position, and their interactions. The simple effect of Register was significant in all 5 word positions, indicating a global elongation effect in CDS. Interestingly, there was no proportional increase of the final word in CDS. In addition, the 3-way interactions Register × Word Position × Focus and Register × Word Position × Sentence Mode were significant, which converge to the conclusion that the utterance-final word in CDS is additively elongated when it is focused and in a statement. CONCLUSION: Elongation in CDS is a global effect, but the additive effects of duration demonstrated in the authors' data suggest that the effect of enhanced utterance-final lengthening in CDS in naturalistic samples may be a by-product of discourse characteristics of CDS.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.001
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 head0.056
GPT teacher head0.377
Teacher spread0.320 · 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 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

Citations10
Published2012
Admission routes2
Has abstractyes

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