ES2ISL: An Advancement in Speech to Sign Language Translation using 3D Avatar Animator
Why this work is in the frame
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Bibliographic record
Abstract
This work proposes a model and an initial implementation of a robust system, which converts English Speech into Indian Sign Language (ES2ISL) animations. Such system may considerably enhance the lives of hearing-impaired people, especially in interaction and information exchange between concerned parties. The core purpose of the system is to bridge the communication gap between hearing-impaired people in India and others. It exploits and integrates the semantics of the Natural Language Processing (NLP), Google cloud speech recognizer API, and a predefined sign language database. The experimental results show that the proposed system outperforms existing models with an average accuracy of 77%. Hence, it overshadows the existing systems in terms of processing time by taking about 0.85s.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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