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Record W4406344031 · doi:10.1121/10.0035261

Three-dimensional finite element acoustic analysis of bent vocal tracts

2024· article· en· W4406344031 on OpenAlexaff
Debasish Ray Mohapatra, Sidney Fels

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBent molecular geometryFinite element methodAcousticsElement (criminal law)Structural engineeringGeologyComputer sciencePhysicsEngineeringPolitical science

Abstract

fetched live from OpenAlex

The bent air column in a vocal tract can significantly impact its acoustic characteristics by altering formant frequencies. However, existing vocal tract models typically assume the tract to be a straight three-dimensional (3D) tube with varying cross-sectional areas. Although wave propagation in realistic vocal tracts has been previously studied, the acoustic effect of the tract curvature on resonance frequencies has yet to be thoroughly explored. In this work, we use a state-of-the-art 3D finite element (FE) wave solver to characterize the effect of bending in acoustic ducts, akin to vocal tracts, by comparing their transfer functions up to 14 kHz. The duct geometries are modified to match vocal tracts for vowels /a/ and /u/. For a comparative analysis, we empirically adjust the degree of bending for a portion of the duct to increase its geometrical complexity. Our result shows that the bent air column does not significantly affect acoustic output for ducts with uniform cross-sections. However, transverse modes appear at higher frequencies for bent vocal tract geometries, i.e., ducts with varying cross-sectional areas. The comparison of transfer functions also shows that above 7 kHz, there is a notable shift in formant frequencies for such duct geometries.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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