Using ASR Technology in Language Training for Specific Purposes
Why this work is in the frame
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Bibliographic record
Abstract
For many patients throughout the world, access to healthcare depends on the patients’ and healthcare providers’ ability to communicate efficiently in each other's language. One way to reduce linguistic barriers to healthcare access is to increase the number of linguistically and culturally competent healthcare professionals. Conspicuously absent in the literature on second language (L2) training of healthcare professionals, however, is the use of technology that combines meaningful interaction, feedback, simulation, and asynchronous access. The goal of this paper is to fill this gap by describing and evaluating the “Virtual Language Patient,” a computer-based L2 training module for healthcare professionals. The module employs automatic speech recognition technology, pronunciation assessment, and video clips of a simulated medical history interview with a minority language patient. Five nurses-in-training at a French-language nursing college in Quebec reported that the module was easy to operate and that it addressed their anticipated language learning needs. More importantly, analysis of the data file automatically generated by the module revealed improvements in acceptability of the nurses’ pronunciation of the medical interview questions. These findings suggest that the module can be effective in language training for healthcare professionals. Implications for the improvement of virtual dialogue systems are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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