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Record W2186546694 · doi:10.1515/lp-2015-0014

Frequency effects on the vowel length contrast merger in Seoul Korean

2015· article· en· W2186546694 on OpenAlexfundno aff
Yoonjung Kang, Tae-Jin Yoon, Sungwoo Han

Bibliographic record

VenueLaboratory Phonology Journal of the Association for Laboratory Phonology · 2015
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsVowelSound changeVowel lengthDuration (music)Contrast (vision)Mid vowelWord (group theory)Speech recognitionWord lists by frequencyLinguisticsMathematicsPsychologyAudiologyAcousticsComputer scienceArtificial intelligencePhysicsFormantMedicine

Abstract

fetched live from OpenAlex

Abstract This paper presents an apparent-time study of the vowel length contrast merger in Seoul Korean based on duration measurements of over 370,000 vowels in word-initial syllables in a read-speech corpus. The effects of word frequency on vowel duration and the lexical diffusion of long-vowel shortening are also examined. The findings confirm the observation made in the previous literature that the vowel length contrast is on its way out in the language, and that this sound change is nearing completion. We also find a significant effect of frequency on long-vowel duration: other things being equal, these vowels are shorter in high-frequency words than in low-frequency words. The rate of change does not differ significantly depending on the frequency of words apart from the high-frequency words reaching the endpoint of change and bottoming out in the change earlier than mid- and low-frequency words. The observed frequency effect is compatible with a model in which the frequency effect on duration comes from on-line factors that affect phonetic implementation of speech sounds, along with an across-the-board lenition bias that drives the sound change, not from stored tokens of word-specific variants.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.003
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 head0.023
GPT teacher head0.300
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations50
Published2015
Admission routes1
Has abstractyes

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