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Record W2770699629 · doi:10.1121/1.5014453

Dialect contact and word-specific phonetics: North Koreans in Seoul

2017· article· en· W2770699629 on OpenAlexaff
Yoonjnung Kang, Suyeon Yun

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVoice-onset timeVowelWord (group theory)PhoneticsLinguisticsHistoryAudiologyGeographyPsychologyMedicine

Abstract

fetched live from OpenAlex

This study examines the speech of North Korean refugees who currently reside in Seoul. We investigate (a) how the North Korean speakers’ stops are affected by contact with Seoul Korean and (b) whether words only commonly used in the North and words newly acquired in the South are realized differently. The data were collected from 35 Hamkyeoung speakers, balanced for age (born before vs. after 1975) and time since arrival in Seoul (0–3 vs. 3–15 years). Twenty Seoul speakers also participated as a comparison group. Participants produced 78 stop-initial words with two repetitions, evenly divided into North vs. South lists, balanced for laryngeal feature, place of articulation, and the following vowel height across the lists. The results show that in line with previous literature, Hamkyeoung aspirated stops are longer in VOT than Seoul stops and Hamkyeoung lenis stops are shorter in VOT than Seoul stops. We found evidence for contact-induced phonetic drift for aspirated stops, but not for lenis stops—earlier arrivals produce aspirated stops with a shorter VOT than recent arrivals. We found no evidence of word-specific phonetic representation and stops were produced with a comparable VOT value regardless of their Northern vs. Southern origin.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.257

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 science0.0010.000
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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 designOther design
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
Published2017
Admission routes1
Has abstractyes

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