Sign Language MMS to Make Cell Phones Accessible to the Deaf and Hard-of-hearing Community.
Bibliographic record
Abstract
Abstract: Cell phones became extremely popular devices considering their vast utility world wide. However, making cell phones accessible to the deaf and hard-of-hearing community is still a challenge. Main available products on the market for this community offer no more than the possibility to boost/amplify volume. Many cellular provides individual cell phone models which are hearing aid compatible and possess speakerphone capabilities. However, if the user is completely deaf, these phones still tend to be somewhat complex or impossible to use. Another alternative, based on Video phones messages, are quickly widespread as the preferred method of communicating for the deaf and hard-of-hearing community. However Video phones require significant computer processing power to compress and decompress video in real time. Nevertheless, this alternative still has to overcome the various technological challenges associated with utilizing video phone technology, especially via low bandwidth network. In this context, this paper describes a new application allowing the use of MMS (Multimedia Messaging Service) to generate sign language animation in order to communicate with deaf people via cell phones. These animations are avatar based animation obtained by automatic interpretation of text into sign language. This application is a new component developed amongst WebSign kernel (Jemni et al., 2007; Jemni and Ellghoul, 2007).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.339 | 0.168 |
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".