Uncertainty analysis of clinically relevant distances derived using photogrammetric intersection for the assessment of motor-speech-control in children
Bibliographic record
Abstract
Abstract. Perceptual analysis is the current benchmark standard used by speech-language pathologists in diagnosing speech sound disorders (SSDs). Yet, related research indicates access to objective measures could improve the assessment process. Recent technological advances have contributed to developing AI-based methods to provide clinicians access to objective measures for speech-motor control by calculating inter-landmark distances of anatomically relevant facial landmarks. However, landmarks placed by AI-based methods extract the landmarks’ coordinates without associated uncertainties. Consequently, inter-landmark distances extracted as objective measurements also lack uncertainty information, potentially compromising their suitability for assessment purposes. In contrast, photogrammetry can predict facial inter-landmark distances and their uncertainties through intersection and variance propagation. In this paper, we use a combination of the markerless BlazeFace algorithm and photogrammetry to examine how different weightings of the image observations, introduced for the photogrammetric intersection, impact the assessment of whether the calculated inter-landmark distances significantly change during the production of spoken words. We selected 16 inter-landmark distances to assess jaw movement. We analysed the movements of five children saying 10 words. Overall, four different weightings and two different camera setups were tested. Setup 1 used 2 cameras, and setup 2 used 3 cameras. The weightings based on comparing the BlazeFace landmarks to a reference were too large when applied to setup 1. They did not allow the reliable determination of inter-landmark distance changes as predicted by current literature depending on the camera setup used. Smaller weights were able to be statistically tested for jaw movements correctly. For setup 2, all weights could detect inter-landmark distances reliably.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".