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Record W4406253632 · doi:10.1017/s0305000924000552

More than just a happy talk? Evidence for functional pitch and utterance length modifications in infant-, spouse-, and dog-directed communication

2025· article· en· W4406253632 on OpenAlexaff
Édua Koós-Hutás, Shanjida Afrin, Alexandra Barbara Kovács, Tamás Faragó, Lőrinc András Filep, József Topál, Anna Gergely

Bibliographic record

VenueJournal of Child Language · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInfant Health and Development
Canadian institutionsNeuroDevNet
FundersNemzeti Kutatási Fejlesztési és Innovációs HivatalMagyar Tudományos AkadémiaHungarian Scientific Research FundBolyai FoundationEuropean Commission
KeywordsUtterancePsychologyRhymeContext (archaeology)SpouseCompetence (human resources)Mean length of utteranceNonverbal communicationCommunicationConversationLinguisticsDevelopmental psychologyCognitive psychologyLanguage developmentSocial psychology

Abstract

fetched live from OpenAlex

By comparing infant-directed speech to spouse- and dog-directed talk, we aimed to investigate how pitch and utterance length are modulated by speakers considering the speech context and the partner's expected needs and capabilities. We found that mean pitch was modulated in line with the partner's attentional needs, while pitch range and utterance length were modulated according to the partner's expected linguistic competence. In a situation with a nursery rhyme, speakers used the highest pitch and widest pitch range with all partners suggesting that infant-directed context greatly influences these acoustic features. Recent findings showed that these speakers expressed more intense positive emotions towards their infants and spouses than towards their dogs. Our results revealed different patterns, leading us to conclude that these acoustic features are not simple by-products of emotional speech. Instead, they are dynamically and functionally used in accordance with the speech context and the audience's expected needs and capabilities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.060
GPT teacher head0.433
Teacher spread0.373 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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