Validation of Camera Networks Used for the Assessment of Speech Movements
Bibliographic record
Abstract
Abstract. The term speech sound disorder describes a range of speech difficulties in children that affect speech intelligibility. Differential diagnosis is difficult and reliant on access to validated and reliable measures. Technological advances aim to provide clinical access to measurements that have been identified as beneficial in diagnosing speech disorders. To generate objective measurements and, consequently, automatic scores, the output from multi-camera networks is required to produce quality results. The quality of photogrammetric results is usually expressed in terms of the precision and reliability of the network. Precision is determined at the design stage as a function of the geometry of the network. In this manuscript, we focus on the design of a photogrammetric camera network using three cameras. We adopted a similar workflow as Alsadika et al. (2012) and tested serval network configurations. As the distances from the camera stations to object points were fixed to 3500mm, only the horizontal and vertical placements of the cameras were varied. Horizontal angles were changed within an increment of 10º, and vertical angles were changed within an increment of 5º. The object space coordinates of GCPs for each camera configuration were assessed in terms of horizontal error ellipses and vertical precision. The best design was the maximum horizontal and vertical convergence angles of 90° and 30°. The existing camera network used to capture videos for speech assessment was approximately as good as the top third of tested designs. However, from a validation perspective, it can be concluded that the design is viable for continued use.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".