Ultrasound-Enhanced Multimodal Approaches to Pronunciation Teaching and Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Second language (L2) pronunciation is one of the most challenging skills to master for adult learners. Accented pronunciation is part of the expression of speakers’ identity, but it could potentially give issues in comprehensibility. Explicit pronunciation instruction from language instructors is often unavailable due to limited class time. Imitating native speakers’ utterances can be done independently from classroom learning, but the absence of feedback makes it difficult for learners to improve their skills. This project takes a multidisciplinary, multimodal approach to pronunciation teaching and learning through a series of video resources, available at http://enunciate.arts.ubc.ca/ . These videos combine external images of a speaker’s head with ultrasound images of their tongue to demonstrate the pronunciation of various sounds. In addition to examples of sounds in isolation, a strong focus to this point has been on the pronunciation of Japanese sounds, with pronunciation instruction incorporating explicit awareness of tongue movements and insights from articulatory phonology. Further stages of the project will include real-time interactive ultrasound tongue visualization and comparative prosody visualization, both of which provide biovisual feedback to L2 learners.
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.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it