  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.
