Dynamic Cross-Feature Fusion for American Sign Language Translation

While a significant amount of work has been done on the commonly used, tightly -constrained weather-based, German sign language (GSL) dataset, little has been done for continuous sign language translation (SLT) in more realistic settings, including American sign language (ASL) translation. Also, while CNN - based features have been consistently shown to work well on the GSL dataset, it is not clear whether such features will work as well in more realistic settings when there are more heterogeneous signers in non-uniform backgrounds. To this end, in this work, we introduce a new, realistic phrase-level ASL dataset (ASLing), and explore the role of different types of visual features (CNN embeddings, human body keypoints, and optical flow vectors) in translating it to spoken American English. We propose a novel Transformer-based, visual feature learning method for ASL translation. We demonstrate the explainability efficacy of our proposed learning methods by visualizing activation weights under various input conditions and discover that the body keypoints are consistently the most reliable set of input features. Using our model, we successfully transfer-learn from the larger GSL dataset to ASLing, resulting in significant BLEU score improvements. In summary, this work goes a long way in bringing together the AI resources required for automated ASL translation in unconstrained environments.