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Record W2026343623 · doi:10.1017/s1366728912000703

Scrutinizing the role of length of residence and age of acquisition in the interlanguage pronunciation development of English /ɹ/ by late Japanese bilinguals

2013· article· en· W2026343623 on OpenAlexaff
Kazuya Saito, François-Xavier Brajot

Bibliographic record

VenueBilingualism Language and Cognition · 2013
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsFormantPronunciationDuration (music)PsychologyInterlanguageSentenceLinguisticsLanguage acquisitionDegree (music)Reading (process)AudiologySpeech recognitionComputer scienceMedicineMathematics education

Abstract

fetched live from OpenAlex

The current project examined whether and to what degree continued L2 input, operationalized as length of residence (LOR), and age of acquisition (AOA), defined as the first intensive exposure to the target language, can be predictive of adult Japanese learners’ production of word-initial English /ɹ/. Data were collected from 65 participants, consisting of three groups of Japanese learners of English (n = 13 for Short-, Mid-, and Long-LOR groups, respectively) and two groups of baseline speakers (n = 13 for Japanese- and English-Baseline groups, respectively). Their production of /ɹ/ was elicited via three oral tasks (i.e., word reading, sentence reading, timed picture description). Acoustic analyses were carried out along four dimensions: third formant (F3), second formant (F2), first formant (F1) frequencies, and formant transition duration. The results demonstrated that (a) all learners reached native-like proficiency with respect to the use of existing cues (F2, transition duration) within approximately one year of LOR, (b) their performance was negatively related to AOA to some degree, and (c) longer LOR was predictive of the development of the new cue (F3). These results suggest that late L2 speech sound acquisition and proficiency may be characterized by different levels of phonetic processing.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.243

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 head0.015
GPT teacher head0.298
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 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

Citations31
Published2013
Admission routes1
Has abstractyes

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