Author manuscript, published in "N/P" Semi-Automatic Extraction of Vocal Tract Movements from Cineradiographic Data
Bibliographic record
Abstract
Since high speed X-ray films still provide the best dynamic view of the entire vocal tract, large existing databases have been preserved and are available for the speech research community. We propose a new technique for facilitating the extraction of the vocal tract shape and the movements of the articulators from complete sequences of these databases. The method was first developed for the extraction of the tongue movements in “Wioland”. It has been adapted to a sequence of the ATR database, Laval43 (provided by Rochette). The method, based on the retromarking algorithm, combines the human expertise applied for marking a small number of key images, and the automatic processing of the video data. It has been extended to other articulators (lips, velum) in order to obtain the shape and the sections of the complete vocal tract. Quantitative evaluations of the estimate error and a comparison with Thimm and Luettin (1999) are achieved. Index terms: semi-automatic extraction, cineradiographic data processing, X-ray film database, vocal tract contour extraction 1.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.220 | 0.114 |
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".