Comparative Analysis of Convolution Neural Network Models for Continuous Indian Sign Language Classification
Bibliographic record
Abstract
Classification of continuous sign language is essential for development of a sign language to spoken language translator. In this paper, classification of continuously signed sentences from the Indian Sign Language is considered using data from one inertial measurement unit placed on each hand of the signer. The recorded accelerometer and gyroscope data are used in tracking the position of hand in three-dimension, which are used as input to the classifier. The time-LeNet and multi-channel deep convolutional neural network (MC-DCNN) are employed for classification of sentences from raw position data of both hands. Moreover, a modified time-LeNet architecture is proposed to address the issue of over-fitting observed in the time-LeNet. The three models are compared for performance in terms of model complexity, loss and classification accuracies. MC-DCNN has large number of trainable parameters and provides an overall accuracy of 83.94%, while time-LeNet yields an average accuracy of 79.70%. The modified time-LeNet yields a classification accuracy of 81.62 % with just sixteenth of trainable parameters as compared to MC-DCNN.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".