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Record W2118300716 · doi:10.1121/1.2173514

Compensation following real-time manipulation of formants in isolated vowels

2006· article· en· W2118300716 on OpenAlexaff
David W. Purcell, Kevin G. Munhall

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsQueen's University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsFormantVowelAuditory feedbackSpeech recognitionComputer scienceLoudnessAcousticsAudiologyCompensation (psychology)Stimulus (psychology)Speech productionPsychologyPhysicsMedicineCognitive psychology

Abstract

fetched live from OpenAlex

Auditory feedback influences human speech production, as demonstrated by studies using rapid pitch and loudness changes. Feedback has also been investigated using the gradual manipulation of formants in adaptation studies with whispered speech. In the work reported here, the first formant of steady-state isolated vowels was unexpectedly altered within trials for voiced speech. This was achieved using a real-time formant tracking and filtering system developed for this purpose. The first formant of vowel /epsilon/ was manipulated 100% toward either /ae/ or /I/, and participants responded by altering their production with average Fl compensation as large as 16.3% and 10.6% of the applied formant shift, respectively. Compensation was estimated to begin <460 ms after stimulus onset. The rapid formant compensations found here suggest that auditory feedback control is similar for both F0 and formants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.271
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations248
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicHearing Loss and RehabilitationFrench-language works237,207