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Record W4220865467 · doi:10.1075/kl.16001.sch

Phonetic cue competition within multiple phonological contrasts

2022· article· en· W4220865467 on OpenAlexafffund
Jessamyn Schertz, Yoonjung Kang

Bibliographic record

VenueKorean Linguistics · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaInha University
KeywordsVowelDuration (music)ConsonantContrast (vision)PsychologyMid vowelPhonologyLinguisticsPhonotacticsStop consonantVoice-onset timeCategorizationVowel lengthPerceptionAudiologyAcousticsFormantComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Abstract This work examines Seoul Korean listeners’ perception of the five Korean sibilants: affricates /c′, c, ch/ and fricatives /s′, s/. Natural productions of the consonants were manipulated to vary orthogonally along several phonetic parameters relevant to the place/manner contrast ((denti)alveolar fricative vs. (palato)alveolar affricate) and the laryngeal contrast (fortis vs. lenis vs. aspirated). Of particular interest was listeners’ representation of /s/, whose laryngeal status is ambiguous. All manipulated parameters (baseline consonant and vowel affiliation, fundamental frequency at vowel onset, frication duration, and aspiration duration) influenced categorization, with consonant and vowel spectral information playing the primary role in distinguishing most sibilants. However, f0, a laryngeal cue, trumped place and manner cues in affricate vs. fricative classification, highlighting the increasing importance of f0 in Korean segmental phonology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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