Guided Learning of Pronunciation by Visualizing Tongue Articulation in Ultrasound Image Sequences
Bibliographic record
Abstract
Ultrasound has been used as one of the primary technologies utilized widely for clinical diagnosis due to its affordability, non-invasive characteristic, portability, and its fast performance in acquisition. Recently, it started to be used as a visual feedback method for tongue articulation, thanks to its capacity of real-time visualization and video capture of underlying structures inside the mouth. When an Ultrasound transducer is placed along the mid-line under a chin, it shows the tongue motion in sagittal view while speaking. As it is still quite difficult to understand the structure in ultrasound images, we proposed a guided learning system for pronunciation by visualizing tongue articulation in Ultrasound image sequences. Video image registration technique has been employed to project sagittal section of tongue back to the corresponding position on the subject head. The proposed system targets speech therapy and foreign language pronunciation lessons. Two main technology components are (i) Ultrasound tongue image segmentation and tracking (ii) registration of Ultrasound image sequences on video of a subject during the speech. Our experiments on Chinese English learners revealed that the proposed system is capable of providing the beneficial improvement on English pronunciation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".