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Record W2981820223 · doi:10.1044/2019_jslhr-h-18-0465

Toddlers Process Common and Infrequent Childhood Mispronunciations Differently for Child and Adult Speakers

2019· article· en· W2981820223 on OpenAlexaff
Dana E. Bernier, Katherine S. White

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAudiologyPerceptionObject (grammar)Contrast (vision)PsychologyCommunicationComputer scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose This study examined toddlers' processing of mispronunciations based on their frequency of occurrence in child speech and the speaker who produced them. Method One hundred twenty 22-month-olds were assigned to 1 of 4 conditions. Using the intermodal preferential looking paradigm, toddlers were shown visual displays containing 1 familiar object and 1 novel object, labeled by either a child or an adult. Familiar objects were labeled correctly or with a small mispronunciation that is either common in child speech (e.g., waisin for raisin) or infrequent (e.g., rauter for water). Results A significant interaction of speaker and type of mispronunciation showed that, for the child speaker, toddlers treated common and infrequent mispronunciations similarly, with equivalently sized mispronunciation penalties relative to correctly pronounced labels. In contrast, for the adult speaker, toddlers showed a large penalty for common mispronunciations, but infrequent mispronunciations were treated equivalently to correct pronunciations. Conclusion These results both reinforce and extend previous work on toddlers' processing of mispronunciations by revealing a complex interplay of speaker, type of mispronunciation, and specific contrast in toddlers' perceptions of mispronunciations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.363

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.377
Teacher spread0.349 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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