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Record W3206149255 · doi:10.16995/labphon.6442

Asymmetries in Perceptual Adjustments to Non-Canonical Pronunciations

2021· article· en· W3206149255 on OpenAlexaff
Molly Babel, Khia A. Johnson, Christina Sen

Bibliographic record

VenueLaboratory Phonology Journal of the Association for Laboratory Phonology · 2021
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVoicePronunciationPerceptionPsychologyAdaptation (eye)LinguisticsCognitive psychologySpeech perceptionSpeech recognitionComputer science

Abstract

fetched live from OpenAlex

This paper examines two plausible mechanisms supporting sound category adaptation: directional shifts towards the novel pronunciation or a general category relaxation of criteria. Focusing on asymmetries in adaptation to the voicing patterns of English coronal fricatives, we suggest that typology or synchronic experience affect adaptation. A corpus study of coronal fricative substitution patterns confirmed that North American English listeners are more likely to be exposed to devoiced /z/ than voiced /s/. Across two perceptual adaptation experiments, listeners in test conditions heard naturally produced devoiced /z/ or voiced /s/ in critical items within sentences, while control listeners were exposed to identical sentences with canonical pronunciations. Perceptual adaptation was tested via a lexical decision test, with devoiced /z/ or voiced /s/, as well as a novel alveopalatalized pronunciation, to determine whether adaptation was targeted in the direction of the exposed variant or reflected a more general relaxation. Results indicate there was directional and word-specific adaptation for /z/-devoicing with no evidence for generalization. Conversely, there was evidence of /s/-voicing generalizing and eliciting general category relaxation. These results underscore the role of perceptual experiences, and support an evaluation stage in perceptual learning, where listeners assess whether to update a representation.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.017
GPT teacher head0.323
Teacher spread0.307 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

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