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Record W3172171392 · doi:10.1121/10.0005006

Physics of phonation offset: Towards understanding relative fundamental frequency observations

2021· article· en· W3172171392 on OpenAlexaff
Mohamed A. Serry, Cara E. Stepp, Sean D. Peterson

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2021
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of Waterloo
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsPhonationCollisionOffset (computer science)MechanicsPhysicsVocal foldsAcousticsComputer scienceAudiologyLarynxMedicine

Abstract

fetched live from OpenAlex

Relative fundamental frequency (RFF) is a promising assessment technique for vocal pathologies. Herein, we explore the underlying laryngeal factors dictating RFF behaviours during phonation offset. To gain physical insights, we analyze a simple impact oscillator model and follow that with a numerical study using the well-established body-cover model of the vocal folds (VFs). Study of the impact oscillator suggests that the observed decrease in fundamental frequency during offset is due, at least in part, to the increase in the neutral gap between the VFs during abduction and the concomitant decrease in collision forces. Moreover, the impact oscillator elucidates a correlation between sharper drops in RFF and increased stiffness of the VFs, supporting experimental RFF studies. The body-cover model study further emphasizes the correlation between the drops in RFF and collision forces. The numerical analysis also illustrates the sensitivity of RFF to abduction initiation time relative to the phase of the phonation cycle, and the abduction period length. In addition, the numerical simulations display the potential role of the cricothyroid muscle to mitigate the RFF reduction. Last, simplified models of phonotraumatic vocal hyperfunction are explored, demonstrating that the observed sharper drops in RFF are associated with increased pre-offset collision forces.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.066
GPT teacher head0.300
Teacher spread0.234 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

Explore more

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