Word-Level American Sign Language (ASL) Translation Using Deep Learning, Leveraging Hand, Face, and Body Key Points
Bibliographic record
Abstract
Employing deep learning models to translate between signed and spoken languages often relies solely on assessing hand movements, dismissing valuable context provided by facial and body expressions. This thesis evaluated the performance of a deep learning model on the word level translation of American Sign Language (ASL) to English, admitting as input videos of human signers. Videos were tokenized via pose estimator as temporal sequences of key points, representing the instantaneous positions of body landmarks. A comparative assessment of translation performance was conducted considering different combinations of key points. Results indicate that the composition of hands and face key points improved translation accuracy by up to 15% over that achievable with hands-only key points. The addition of body key points yielded minimal gains, and at times, was detrimental to accuracy, suggesting that the ideal input space at the ASL word level was the combination of hands and facial key points.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".