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Record W2802963291 · doi:10.1121/1.5032199

Effects of language experience and task demands on talker recognition by children and adults

2018· article· en· W2802963291 on OpenAlexafffund
Natalie Fecher, Elizabeth K. Johnson

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTask (project management)Computer scienceSpeech recognitionLinguisticsCognitive psychologyAcousticsPsychologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Talker recognition is a language-dependent process, with listeners recognizing talkers better when the talkers speak a familiar versus an unfamiliar language. This language familiarity effect (LFE) is firmly established in adults, but its developmental trajectory in children is not well understood. Some evidence suggests that the effect already exists in infancy, but little is known about how it unfolds in childhood. The present study explored whether the strength of the LFE increases in early childhood. Adults and children were tested in their native language and a foreign language using a "same-different" talker discrimination task and a "voice line-up" talker recognition task. Results showed that adults and 6-year-olds, but not 5-year-olds, exhibit a robust LFE, suggesting that the effect strengthens as children's language competence increases. For both adults and older children, the emergence of an LFE moreover appeared to be task-dependent. This study contributes to a better understanding of how children develop mature talker recognition abilities and when children's processing of indexical and linguistic information in speech approaches adult-like levels. Furthermore, the findings reported here contribute to the debates regarding the origins of the LFE-a hallmark of adult talker recognition.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.517

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 science0.0000.000
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 head0.007
GPT teacher head0.290
Teacher spread0.283 · 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

Citations27
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicPhonetics and Phonology ResearchFrench-language works237,207