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Record W2740819149 · doi:10.1121/1.4991348

The articulatory dynamics of pre-velar and pre-nasal /æ/-raising in English: An ultrasound study

2017· article· en· W2740819149 on OpenAlexfundaboutno aff
Jeff Mielke, Christopher Carignan, Erik R. Thomas

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
FundersCollege of Humanities and Social Sciences, United Arab Emirates UniversityUniversity of OttawaNorth Carolina State UniversityNational Science Foundation
KeywordsRaising (metalworking)NasalizationVoiceDynamics (music)ConsonantGestureMandarin ChineseAmerican EnglishVariation (astronomy)North American EnglishSpeech recognitionAcousticsLinguisticsTongueVoice-onset timeComputer scienceVowelFormantMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Most dialects of North American English exhibit /æ/-raising in some phonological contexts. Both the conditioning environments and the temporal dynamics of the raising vary from region to region. To explore the articulatory basis of /æ/-raising across North American English dialects, acoustic and articulatory data were collected from a regionally diverse group of 24 English speakers from the United States, Canada, and the United Kingdom. A method for examining the temporal dynamics of speech directly from ultrasound video using EigenTongues decomposition [Hueber, Aversano, Chollet, Denby, Dreyfus, Oussar, Roussel, and Stone (2007). in IEEE International Conference on Acoustics, Speech and Signal Processing (Cascadilla, Honolulu, HI)] was applied to extract principal components of filtered images and linear regression to relate articulatory variation to its acoustic consequences. This technique was used to investigate the tongue movements involved in /æ/ production, in order to compare the tongue gestures involved in the various /æ/-raising patterns, and to relate them to their apparent phonetic motivations (nasalization, voicing, and tongue position).

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.567
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.351
Teacher spread0.327 · 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 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
Published2017
Admission routes2
Has abstractyes

Explore more

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