Designing a Multi-Modal Communication System for the Deaf and Hard-of-Hearing Users
Bibliographic record
Abstract
In remote collaboration using Augmented Reality (AR), speech and gesture are major communication methods for the general public. However, the Deaf and Hard-of-Hearing (DHH) population cannot join in the communication due to the absence of a sign language interface which is their primary language. Recent works have tried to augment spoken language with sign language animations or captions, but the research to convey sign language with spoken language is still very limited. In this paper, we propose a novel multi-modal communication system that integrates sign language translation, speech recognition, and shared object manipulation in the mobile AR environment. Though the system is currently under development, we demonstrated a rapid prototype of the telemedicine app leveraging the video prototyping method to integrate the system modules. We performed preliminary interviews about our approach with DHH users, a sign language interpreter, and a physician. We discuss the insights into the future design of the DHH communication support in the AR collaboration system. This study has a socio-cultural, economic impact on the DHH population as a barrier-free design of a remote collaboration system in a practical scenario. Another contribution of this work is that we suggested a novel user-centered system for DHH users in AR by integrating the existing technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".