Measuring Tongue Shapes and Positions with Ultrasound Imaging: A Validation Experiment Using an Articulatory Model
Bibliographic record
Abstract
OBJECTIVE: The goal of this paper is to assess the validity of various metrics developed to characterize tongue shapes and positions collected through ultrasound imaging in experimental setups where the probe is not constrained relative to the subject's head. PATIENTS AND METHODS: Midsagittal contours were generated using an articulatory-acoustic model of the vocal tract. Sections of the tongue were extracted to simulate ultrasound imaging. Various transformations were applied to the tongue contours in order to simulate ultrasound probe displacements: vertical displacement, horizontal displacement, and rotation. The proposed data analysis method reshapes tongue contours into triangles and then extracts measures of angles, x and y coordinates of the highest point of the tongue, curvature degree, and curvature position. RESULTS: Parameters related to the absolute tongue position (tongue height and front/back position) are more sensitive to horizontal and vertical displacements of the probe, whereas parameters related to tongue curvature are less sensitive to such displacements. CONCLUSION: Because of their robustness to probe displacements, parameters related to tongue shape (especially curvature) are particularly well suited to cases where the transducer is not constrained relative to the head (studies with clinical populations or children).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".