MétaCan
Menu
Back to cohort
Record W4400288297 · doi:10.1121/10.0027203

Exploring infant talker bias: Insights from remote speech perception testing

2024· article· en· W4400288297 on OpenAlexaff
M. Fernanda Alonso Arteche, Nicola Phillips, Samin Moradi, Lulan Shen, Marianne Chen-Ouellet, Leatisha Ramloll, Lei Zeng, Lucie MENARD, Linda Polka

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversité du Québec à MontréalMcGill University
Fundersnot available
KeywordsSpeech perceptionPerceptionPsychologyCognitive psychologyAudiologySpeech recognitionComputer scienceMedicineNeuroscience

Abstract

fetched live from OpenAlex

Lab studies show that infants (4- to 7-month-olds) prefer to listen to vowels with infant-like f0 and formant frequencies over those of an adult female (Masapollo et al., 2015; Polka et al., 2021). This Infant Talker Bias may facilitate infants’ mapping of articulatory gestures to acoustic correlates. In this study, 4- to 12-month-olds completed a listening preference task on the Lookit online testing platform. Across eight trials, we presented synthesized infant and adult vowel sounds (/i/ and /a/) paired with a simple animation and recorded the infant’s response via the webcam. Infant looking time and vocalization to each vowel type were coded offline. Preliminary analyses (n = 91) show that listening time increased with age (p < 0.05), and all infants listened longer to infant vowels than to adult vowels (p < 0.01). Preliminary analyses (n = 62) also show an increase in infant vocalizations with age (p = 0.00) and a trend towards more and longer vocalizations in response to the adult vowels. These findings replicate and extend the infant talker bias to new vowel stimuli and to older infants, and support the use of remote testing in infant speech perception studies.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.143
GPT teacher head0.296
Teacher spread0.153 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicLanguage, Discourse, Communication StrategiesFrench-language works237,207