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Record W2082887517 · doi:10.1121/1.3588068

Feed-forward control of phonetic gestures in consonant–vowel syllables: Evidence from responses to auditory startle.

2011· article· en· W2082887517 on OpenAlexaff
Chenhao Chiu, Andrew James Thomas Stevenson, Dana Maslovat, Romeo Chua, Bryan Gick, Ian M. Franks

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2011
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAuditory feedbackVowelFormantStimulus (psychology)Speech productionConsonantAudiologyGestureSpeech recognitionComputer sciencePsychologyAcousticsCommunicationCognitive psychologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Speech production like other limb movements relies on both feed-forward and feedback mechanisms. Use of a startling auditory stimulus (>90 dB) has been shown to trigger fast, accurate feed-forward performances in upper limb movements prior to access to feedback information [Valls-Solé et al. (1999), J. Physiol. 516: 931–938; Carlsen et al. (2004), J. Mot. Behav. 36: 253–264]. This startle paradigm is applied to test whether pre-programed, feed-forward speech production differs in phonetic detail from production with access to feedback. The experiment examined the production of the CV syllable [ba], starting with the mouth either open or closed. This speech production was triggered either by a control stimulus (82 dB) or by a startling stimulus (124 dB). Results from ten participants showed that lip compression occurred for both starting conditions (mouth open and mouth closed), and also indicated that the timing relationships of the articulators were stable across control trials and startle trials. The acoustics of syllables, formant values, and maximum amplitudes, were consistent across control trials and startle trials, suggesting that the production of pre-programmed gestural configurations in CV syllables may be executed under exclusively feed-forward control.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.320

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 science0.0010.000
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 head0.043
GPT teacher head0.309
Teacher spread0.266 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicHearing Impairment and CommunicationFrench-language works237,207