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Record W2060188405 · doi:10.1121/1.3278606

Compensations in response to real-time formant perturbations of different magnitudes

2010· article· en· W2060188405 on OpenAlexafffund
Ewen MacDonald, Robyn Goldberg, Kevin G. Munhall

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsQueen's University
FundersNational Institute on Deafness and Other Communication DisordersNatural Sciences and Engineering Research Council of Canada
KeywordsFormantPerturbation (astronomy)VowelAmplitudeAuditory feedbackAcousticsPhysicsAudiologyMathematicsComputer scienceSpeech recognitionOpticsMedicineQuantum mechanics

Abstract

fetched live from OpenAlex

Previous auditory perturbation studies have demonstrated that talkers spontaneously compensate for real-time formant-shifts by altering formant production in a manner opposite to the perturbation. Here, two experiments were conducted to examine the effect of amplitude of perturbation on the compensatory behavior for the vowel /epsilon/. In the first experiment, 20 male talkers received three step-changes in acoustic feedback: F1 was increased by 50, 100, and 200 Hz, while F2 was simultaneously decreased by 75, 125, and 250 Hz. In the second experiment, 21 male talkers received acoustic feedback in which the shifts in F1 and F2 were incremented by +4 and -5 Hz on each utterance to a maximum of +350 and -450 Hz, respectively. In both experiments, talkers altered production of F1 and F2 in a manner opposite to that of the formant-shift perturbation. Compensation was approximately 25%-30% of the perturbation magnitude for shifts in F1 and F2 up to 200 and 250 Hz, respectively. As larger shifts were applied, compensation reached a plateau and then decreased. The similarity of results across experiments suggests that the compensatory response is dependent on the perturbation magnitude but not on the rate at which the perturbation is introduced.

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)0.000
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 score0.972
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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

Citations89
Published2010
Admission routes2
Has abstractyes

Explore more

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