Context Matters: Self-Attention for Sign Language Recognition

12 Jan 2021  ·  Fares Ben Slimane, Mohamed Bouguessa ·

This paper proposes an attentional network for the task of Continuous Sign Language Recognition. The proposed approach exploits co-independent streams of data to model the sign language modalities. These different channels of information can share a complex temporal structure between each other. For that reason, we apply attention to synchronize and help capture entangled dependencies between the different sign language components. Even though Sign Language is multi-channel, handshapes represent the central entities in sign interpretation. Seeing handshapes in their correct context defines the meaning of a sign. Taking that into account, we utilize the attention mechanism to efficiently aggregate the hand features with their appropriate spatio-temporal context for better sign recognition. We found that by doing so the model is able to identify the essential Sign Language components that revolve around the dominant hand and the face areas. We test our model on the benchmark dataset RWTH-PHOENIX-Weather 2014, yielding competitive results.

PDF Abstract
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Sign Language Recognition RWTH-PHOENIX-Weather 2014 SAN Word Error Rate (WER) 29.7 # 13


No methods listed for this paper. Add relevant methods here