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Record W2154909174 · doi:10.1109/icassp.2004.1326054

Estimating vocal-tract area functions from vowel sound signals over closed glottal phases

2004· article· en· W2154909174 on OpenAlexaff
Huiqun Deng, Rabab Ward, M.P. Beddoes, Murray Hodgson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVocal tractSpeech recognitionVowelComputer scienceAcousticsGlottisIdeal (ethics)MathematicsLarynxMedicinePhysicsAnatomy

Abstract

fetched live from OpenAlex

Existing methods that estimate the vocal-tract area functions (VTAF) from vocal-tract filters (VTF) using speech signals suffer from inadequate elimination of the glottal wave, and the influence of non-ideal vocal-tract boundary conditions. To minimize these effects on the VTF estimation, we present a method that jointly estimates the glottal wave and the VTF corresponding to closed glottal phases. Experimental results show that our method yields better estimates. The VTAF obtained for /a/ and /i/ each produced by a female and a male subject show that more detailed and accurate VTAF are obtained using the speech signals corresponding to closed glottal phases.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.041
GPT teacher head0.277
Teacher spread0.236 · 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.

Study designOther design
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

Citations3
Published2004
Admission routes1
Has abstractyes

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