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Record W1494392095 · doi:10.1121/1.4786663

Infant-directed speech: Final syllable lengthening and rate of speech

2005· article· en· W1494392095 on OpenAlexaff
Robyn Church, Barbara May Bernhardt, Rushen Shi, M. Kathleen Pichora‐Fuller

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of TorontoUniversité du Québec à MontréalUniversity of British Columbia
Fundersnot available
KeywordsSyllableUtteranceDuration (music)Speech recognitionPhraseComputer sciencePsychologyLinguisticsNatural language processingAcousticsPhysics

Abstract

fetched live from OpenAlex

Speech rate has been reported to be slower in infant-directed speech (IDS) than in adult-directed speech (ADS). Studies have also found phrase-final lengthening to be more exaggerated in IDS compared with ADS. In our study we asked whether the observed overall slower rate of IDS is due to exaggerated utterance-final syllable lengthening. Two mothers of preverbal English-learning infants each participated in two recording sessions, one with her child, and another with an adult friend. The results showed an overall slower rate in IDS compared to ADS. However, when utterance-final syllables were excluded from the calculation, the speech rate in IDS and ADS did not differ significantly. The duration of utterance-final syllables differed significantly for IDS versus ADS. Thus, the overall slower rate of IDS was due to the extra-long final syllable occurring in relatively short utterances. The comparable pre-final speech rate for IDS and ADS further accentuates the final syllable lengthening in IDS. As utterances in IDS are typically phrases or clauses, the particularly strong final-lengthening cue could potentially facilitate infants’ segmentation of these syntactic units. These findings are consistent with the existing evidence that pre-boundary lengthening is important in the processing of major syntactic units in English-learning infants.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.718

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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 designNot applicable
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

Citations18
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicLanguage Development and DisordersFrench-language works237,207