Ultrasound-Enhanced Multimodal Approaches to Pronunciation Teaching and Learning
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".