The Haskins Optically Corrected Ultrasound System (HOCUS)
Bibliographic record
Abstract
The tongue is critical in the production of speech, yet its nature has made it difficult to measure. Not only does its ability to attain complex shapes make it difficult to track, it is also largely hidden from view during speech. The present article describes a new combination of optical tracking and ultrasound imaging that allows for a noninvasive, real-time view of most of the tongue surface during running speech. The optical system (Optotrak) tracks the location of external structures in 3-dimensional space using infrared emitting diodes (IREDs). By tracking 3 or more IREDs on the head and a similar number on an ultrasound transceiver, the transduced image of the tongue can be corrected for the motion of both the head and the transceiver and thus be represented relative to the hard structures of the vocal tract. If structural magnetic resonance images of the speaker are available, they may allow the estimation of the location of the rear pharyngeal wall as well. This new technique is contrasted with other currently available options for imaging the tongue. It promises to provide high-quality, relatively low-cost imaging of most of the tongue surface during fairly unconstrained speech.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.027 | 0.005 |
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".