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Record W2150564659 · doi:10.1121/1.1473636

Control of oral closure in lingual stop consonant production

2002· article· en· W2150564659 on OpenAlexaff
Anders Löfqvist, Vincent L. Gracco

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsTongueClosure (psychology)KinematicsVowelMovement (music)ConsonantStop consonantSpeech productionContext (archaeology)MathematicsAcousticsComputer scienceMedicineSpeech recognitionPhysicsGeology

Abstract

fetched live from OpenAlex

Previous work has shown that the lips are moving at a high velocity when the oral closure occurs for bilabial stop consonants, resulting in tissue compression and mechanical interactions between the lips. The present experiment recorded tongue movements in four subjects during the production of velar and alveolar stop consonants to examine kinematic events before, during, and after the stop closure. The results show that, similar to the lips, the tongue is often moving at a high velocity at the onset of closure. The tongue movements were more complex, with both horizontal and vertical components. Movement velocity at closure and release were influenced by both the preceding and the following vowel. During the period of oral closure, the tongue moved through a trajectory of usually less than 1 cm; again, the magnitude of the movement was context dependent. Overall, the tongue moved in forward-backward curved paths. The results are compatible with the idea that the tongue is free to move during the closure as long as an airtight seal is maintained. A new interpretation of the curved movement paths of the tongue in speech is also proposed. This interpretation is based on the principle of cost minimization that has been successfully applied in the study of hand movements in reaching.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.254
Teacher spread0.227 · 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 designBench or experimental
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

Citations72
Published2002
Admission routes1
Has abstractyes

Explore more

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