Glottal-wave and vocal-tract-area-function estimations from vowel sounds based on realistic assumptions and models
Bibliographic record
Abstract
Estimating glottal waves by inverse filtering vowel sounds and deriving vocal-tract area functions (VTAFs) from vocal-tract filter (VTF) estimates require that VTF models be realistic and that VTF estimates contain no effects of open glottises and glottal waves. In this study, VTFs are modeled to have lip reflection coefficients with low-pass frequency responses; to minimize the effects of open glottises and glottal waves on the estimates, VTFs are estimated from sustained vowel sounds over closed glottal phases, assuming that the glottal waves are periodically stationary random processes. Since incomplete glottal closures are common, VTF estimates may contain the effects of glottal loss. To eliminate the effects of glottal loss in the VTF estimates, lip-opening areas must be known. Theoretically, estimates of glottal waves and VTAFs corresponding to large-lip-opening vowel sounds are less affected by the glottal loss than those corresponding to small-lip-opening vowel sounds. The VTAFs and glottal waves estimated from vowel sounds produced by several subjects are presented. The normalized VTAFs estimated from large-lip-opening sounds are similar to that measured from an unknown subjects magnetic resonance image. Over closed glottal phases, the glottal waves are non-zero. They increase during vocal-fold colliding, and decrease or even increase during vocal-fold parting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".