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Record W4253827017 · doi:10.1121/1.4800732

Evaluation of contact pressure in human vocal folds during phonation using high-speed videoendoscopy, electroglottography, and magnetic resonance imaging

2013· article· en· W4253827017 on OpenAlexaff
Zhe Li, Hani Bakhshaee, Leah B. Helou, Luc Mongeau, Karen Kost, Clark A. Rosen, Katherine Verdolini

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhonationElectroglottographVocal foldsAmplitudeAcousticsMicrophoneArticulatory phoneticsMaterials sciencePhysicsSound pressureOpticsAudiologyAnatomyMedicineLarynx

Abstract

fetched live from OpenAlex

Mechanical stresses on the vocal folds surface during high pitch or amplitude phonation have been postulated to cause vocal fold damage. Models for the quantitative estimate of the contact pressure may be valuable for prevention and treatment. The objective of this study was to non-invasively estimate the contact pressure for different phonation types, frequencies and amplitudes in human subjects using concurrent high-speed videoendoscopy and eletroglottography. The edge velocities before and after contact were estimated from the analysis of consecutive digital images. Instantaneous contact areas were determined from electroglottography along with Magnetic resonance image (MRI). The contact pressure was assessed using the impulse momentum form of Newton's second law. Investigations were carried out in quantitative human subjects to compare contact pressures for three different voice types, frequencies and amplitude levels. Contact pressures for breathy, normal and pressed voice were obtained for five subjects. The results were verified through comparisons with values measured directly using a probe microphone. The proposed method appears to be robust and accurate for contact pressure estimation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.810

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 studies0.0000.000
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 head0.012
GPT teacher head0.272
Teacher spread0.260 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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