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Record W4214819577 · doi:10.1121/1.4798779

Acoustic correlates of flaps in North American English

2013· article· en· W4214819577 on OpenAlexaff
Donald Derrick, Benjamin G. Schultz

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoarticulationVowelComputer scienceSpeech recognitionCategorical variableContext (archaeology)American EnglishHierarchical clusteringSmoothingCluster analysisArtificial intelligenceLinguisticsGeographyMachine learningComputer vision

Abstract

fetched live from OpenAlex

Using B/M mode ultrasound, Derrick & Gick (2010) identified four categorical variations of flaps in North American English, up-flaps, down-flaps, alveolar taps, and postalveolar taps produced in English. These variants can be used to test hypotheses about constraints on speech articulation, such as local context, gravity and elasticity, speech rate, and longer distance anticipatory coarticulation. This study examines acoustic correlates of flap variations in order to make connections between the results of larger, and easier to collect, acoustic databases and the tongue movements underlying flap productions. Preliminary analyses using smoothing spline ANOVAs of z-score normalized f0, F1, F2, F3, F4 and F5 indicate significant differences in each dependent variable for flaps in non-rhotic vowel contexts. The results for flaps adjacent to rhotic vowels is more complex, requiring more detailed analysis. Based on these results, we are currently planning supervised hierarchical clustering to aid in probabilistic identification of flap variants, with reference to both vocalic context and syllable structure.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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