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Vowel inventory size matters: assessing cue-weighting in L2 vowel perception

2017· article· en· W2756856085 on OpenAlexaff
Hanna Kivistö de Souza, Angélica Carlet, Izabela Anna Jułkowska, Anabela Rato

Bibliographic record

VenueIlha do Desterro A Journal of English Language Literatures in English and Cultural Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVowelPsychologyPerceptionDanishDiphthongMid vowelDuration (music)CatalanWeightingAudiologyLinguisticsVowel lengthSpeech perceptionFormantMedicine

Abstract

fetched live from OpenAlex

To examine whether L1 vowel inventory size could be a contributing factor to the use of temporal cues in L2 vowel perception, this study assessed the perception of English /i-ɪ/ by 66 learners of four different L1s: Danish, Portuguese, Catalan and Russian. The L2 learners performed a forced-choice identification task containing natural and duration-manipulated stimuli. Findings suggest that the participants’ over-reliance on duration cues seem to be partially related to their L1 vowel inventory size. The participants with the greatest L1 vowel inventory (Danish) demonstrated the most native-like vowel perception and the participants with the smallest L1 vowel inventory (Russian) over-relied on duration cues more than the other learners. Interestingly, the participants with somewhat comparable L1 vowel inventories (Portuguese and Catalan) performed similarly.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.032
GPT teacher head0.381
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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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