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Record W4281551964 · doi:10.33137/twpl.v44i1.36758

Phonological acquisition of European Portuguese oral vowels: A forced-choice identification study with Hungarian native speakers

2022· article· en· W4281551964 on OpenAlexfundvenueno aff
Gabriela Tavares, Andrea Deme, Susana Correia

Bibliographic record

VenueToronto Working Papers in Linguistics · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersNemzeti Kutatási, Fejlesztési és Innovaciós AlapUniversidade de LisboaMagyar Tudományos AkadémiaFundação para a Ciência e a TecnologiaInnovációs és Technológiai MinisztériumFederation for the Humanities and Social Sciences
KeywordsEuropean PortugueseCategorizationPsychologyPortugueseLinguisticsPerceptionScope (computer science)Identification (biology)Assimilation (phonology)Variety (cybernetics)Brazilian PortugueseSpeech recognitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Auditory perceptual experiments with Portuguese L2 are scarce, especially within the scope of the European variety (EP). In this study, we aim at observing the assimilation of the EP vowels [ɐ] and [ɨ] by Hungarian native speakers, and their perceptual learning after some contact with Portuguese. A multiple forced-choice identification experiment was run with two groups of Hungarian speakers – a group with and a group without previous contact with EP. The results show that the categorization fell into the closest phonetic categories of the L1. The results also indicate a learning effect for [ɨ], with a recategorization path from [y] to [ø]. As for [ɐ], no major differences were observed between groups. This result is consistent with difficulties observed in the classroom environment, where [ɐ] is particularly challenging to Hungarian learners.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.041
GPT teacher head0.340
Teacher spread0.298 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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