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Record W2162523782

영어어구의 위치에 따른 단어의 음향 변수 측정

2007· article· ko· W2162523782 on OpenAlexaboutno aff
양병곤

Bibliographic record

Venue음성과학 · 2007
Typearticle
Languageko
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhraseFormantWord (group theory)Speech recognitionPronunciationDuration (music)Stress (linguistics)Computer scienceLinguisticsArtificial intelligenceAcousticsVowelPhysics
DOInot available

Abstract

fetched live from OpenAlex

The purposes of this paper were to develop an automatic script to collect such acoustic parameters as duration, intensity, pitch and the first two formant values of English words produced by two native Canadian speakers either alone or in a two-word phrase at a normal speed and to compare those values by the position in the phrases. A Praat script was proposed to obtain the comparable parameters at evenly divided time point of the target word. Results showed that the total duration of the word in the phrase was shorter than that of the word produced alone. That was attributed to the pronunciation style of the native speakers generally placing the primary word stress in the first word position. Also, the reduction ratio of the male speaker depended on the word position in the phrase while the female speaker didn't. Moreover, there were different contours of intensity and pitch by the position of the target word in the phrase while almost the same formant patterns were observed. Further studies would be desirable to examine those parameters of the words in the authentic speech materials.

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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.049
GPT teacher head0.414
Teacher spread0.365 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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