  Sign Language Translation (SLT) is a task that has not been studied
relatively much compared to the study of Sign Language Recognition (SLR).
However, the SLR is a study that recognizes the unique grammar of sign
language, which is different from the spoken language and has a problem that
non-disabled people cannot easily interpret. So, we're going to solve the
problem of translating directly spoken language in sign language video. To this
end, we propose a new keypoint normalization method for performing translation
based on the skeleton point of the signer and robustly normalizing these points
in sign language translation. It contributed to performance improvement by a
customized normalization method depending on the body parts. In addition, we
propose a stochastic frame selection method that enables frame augmentation and
sampling at the same time. Finally, it is translated into the spoken language
through an Attention-based translation model. Our method can be applied to
various datasets in a way that can be applied to datasets without glosses. In
addition, quantitative experimental evaluation proved the excellence of our
method.
