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Record W2081866590 · doi:10.1121/1.2266460

Developmental and cross-linguistic variation in the infant vowel space: The case of Canadian English and Canadian French

2006· article· en· W2081866590 on OpenAlexafffundabout
Susan Rvachew, Karen Mattock, Linda Polka, Lucie Ménard

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversité du Québec à MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVowelFormantMid vowelVariation (astronomy)Vocal tractLinguisticsSpace (punctuation)MathematicsPsychologyAudiologyMedicinePhysicsAstrophysics

Abstract

fetched live from OpenAlex

This article describes the results of two experiments. Experiment 1 was a cross-sectional study designed to explore developmental and cross-linguistic variation in the vowel space of 10- to 18-month-old infants, exposed to either Canadian English or Canadian French. Acoustic parameters of the infant vowel space were described (specifically the mean and standard deviation of the first and second formant frequencies) and then used to derive the grave, acute, compact, and diffuse features of the vowel space across age. A decline in mean F1 with age for French-learning infants and a decline in mean F2 with age for English-learning infants was observed. A developmental expansion of the vowel space into the high-front and high-back regions was also evident. In experiment 2, the Variable Linear Articulatory Model was used to model the infant vowel space taking into consideration vocal tract size and morphology. Two simulations were performed, one with full range of movement for all articulatory paramenters, and the other for movement of jaw and lip parameters only. These simulated vowel spaces were used to aid in the interpretation of the developmental changes and cross-linguistic influences on vowel production in experiment 1.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.627

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 studies0.0000.001
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 head0.013
GPT teacher head0.286
Teacher spread0.274 · 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

Citations46
Published2006
Admission routes3
Has abstractyes

Explore more

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