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Lip Movement Exaggerations During Infant-Directed Speech

2010· article· en· W2155034926 on OpenAlexaff
Jordan R. Green, Ignatius S. B. Nip, Erin M. Wilson, Antje S. Mefferd, Yana Yunusova

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2010
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsFormantVowelAudiologyLower lipDuration (music)PsychologyMovement (music)Speech recognitionCommunicationAcousticsComputer scienceMedicine

Abstract

fetched live from OpenAlex

PURPOSE: Although a growing body of literature has identified the positive effects of visual speech on speech and language learning, oral movements of infant-directed speech (IDS) have rarely been studied. This investigation used 3-dimensional motion capture technology to describe how mothers modify their lip movements when talking to their infants. METHOD: Lip movements were recorded from 25 mothers as they spoke to their infants and other adults. Lip shapes were analyzed for differences across speaking conditions. The maximum fundamental frequency, duration, acoustic intensity, and first and second formant frequency of each vowel also were measured. RESULTS: Lip movements were significantly larger during IDS than during adult-directed speech, although the exaggerations were vowel specific. All of the vowels produced during IDS were characterized by an elevated vocal pitch and a slowed speaking rate when compared with vowels produced during adult-directed speech. CONCLUSION: The pattern of lip-shape exaggerations did not provide support for the hypothesis that mothers produce exemplar visual models of vowels during IDS. Future work is required to determine whether the observed increases in vertical lip aperture engender visual and acoustic enhancements that facilitate the early learning of speech.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.417
Teacher spread0.363 · 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 designBench or experimental
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

Citations113
Published2010
Admission routes1
Has abstractyes

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