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Record W4205091055 · doi:10.1017/s0952675721000269

Articulatory coordination distinguishes complex segments from segment sequences

2021· article· en· W4205091055 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhonology · 2021
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGestureKinematicsVariation (astronomy)Speech productionComputer scienceSpeech recognitionArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Phonological patterning motivates a distinction between complex segments and segment sequences, although it has also been suggested that there might be reliable phonetic differences. We develop the hypothesis that, in addition to their distinct phonological patterning, complex segments differ from segment sequences in how constituent articulatory gestures are coordinated in time. Through computational simulation, we illustrate predictions that follow from hypothesised coordination differences, showing as well how coordination is conceptually independent of temporal duration. We test predictions with kinematic data collected using electromagnetic articulography. Electromagnetic articulography data comparing labial-palatal gestures in Russian, which we argue on the basis of phonological facts to constitute complex segments, and similar labial-palatal gestures in English, which we argue constitute segment sequences, show distinct patterns of coordination, providing robust support for our main hypothesis. At least in this case, gestural coordination conditions patterns of kinematic variation that clearly distinguish complex segments from segment sequences.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.002

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 head0.076
GPT teacher head0.362
Teacher spread0.286 · 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