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Record W2026847862 · doi:10.1121/1.3158600

The sensitivity of auditory-motor representations to subtle changes in auditory feedback while singing

2009· article· en· W2026847862 on OpenAlexafffund
Dwayne Keough, Jeffery A. Jones

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsWilfrid Laurier University
FundersNational Institute on Deafness and Other Communication DisordersNatural Sciences and Engineering Research Council of Canada
KeywordsAuditory feedbackSingingAudiologyVowelPsychologyAcousticsSpeech recognitionComputer scienceMedicinePhysics

Abstract

fetched live from OpenAlex

Singing requires accurate control of the fundamental frequency (F0) of the voice. This study examined trained singers' and untrained singers' (nonsingers') sensitivity to subtle manipulations in auditory feedback and the subsequent effect on the mapping between F0 feedback and vocal control. Participants produced the consonant-vowel /ta/ while receiving auditory feedback that was shifted up and down in frequency. Results showed that singers and nonsingers compensated to a similar degree when presented with frequency-altered feedback (FAF); however, singers' F0 values were consistently closer to the intended pitch target. Moreover, singers initiated their compensatory responses when auditory feedback was shifted up or down 6 cents or more, compared to nonsingers who began compensating when feedback was shifted up 26 cents and down 22 cents. Additionally, examination of the first 50 ms of vocalization indicated that participants commenced subsequent vocal utterances, during FAF, near the F0 value on previous shift trials. Interestingly, nonsingers commenced F0 productions below the pitch target and increased their F0 until they matched the note. Thus, singers and nonsingers rely on an internal model to regulate voice F0, but singers' models appear to be more sensitive in response to subtle discrepancies in auditory feedback.

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)0.002
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 score0.927
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.024
GPT teacher head0.329
Teacher spread0.305 · 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

Citations42
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicPhonetics and Phonology ResearchFrench-language works237,207