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Record W2093861142

Perception of natural vowels by monolingual Canadian-English, Mexican-Spanish, and Penninsular-Spanish listeners

2012· article· en· W2093861142 on OpenAlexvenueaboutno aff
Geoffrey Stewart Morrison

Bibliographic record

VenueCanadian acoustics · 2012
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionVowelLinguisticsContrast (vision)PsychologyAmerican EnglishVowel lengthAffect (linguistics)GeographyCommunicationComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

On the basis of a previously-reported synthetic-vowel perception experiment, it was hypothesized that the location of the perceptual boundary between Spanish /i/ and /e/ differed for monolingual Peninsular-Spanish and Mexican-Spanish listeners (north-central Spain and Mexico City), and that this would affect the perception of the Canadian-English /i/-/i/ contrast (western Canada): Peninsular-Spanish listeners were predicted to identify almost all tokens of Canadian-English /i/ as Spanish /i/ and almost all tokens of Canadian-English /i/ as Spanish /e/ (two-category assimilation); whereas Mexican-Spanish listeners were predicted to identify almost all tokens of Canadian-English /i/ as Spanish /i/, but identify some tokens of Canadian-English /i/ as Spanish /i/ and some as Spanish /e/. Monolingual Peninsular-Spanish and Mexi-can-Spanish listeners’ perception of natural tokens of English /i/, /i/, /e/, and /e/ produced by monolingual Canadian-English speakers was tested. Both the Peninsular-Spanish and the Mexican-Spanish listeners had results consistent with the perceptual pattern predicted for the Peninsular-Spanish listeners. The results call into question the assumption that first-language-Spanish learners of English have difficulty learning the English /i/-/i/ contrast because they initially assimilate most tokens of both English vowel categories to a single Spanish vowel category, Spanish /i/.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.282
Teacher spread0.268 · 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.

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

Citations0
Published2012
Admission routes2
Has abstractyes

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