Keypoint based Sign Language Translation without Glosses

• URL: http://arxiv.org/abs/2204.10511v1
• Date: Fri, 22 Apr 2022 05:37:56 GMT
• Title: Keypoint based Sign Language Translation without Glosses
• Authors: Youngmin Kim, Minji Kwak, Dain Lee, Yeongeun Kim, Hyeongboo Baek
• Abstract summary: We propose a new keypoint normalization method for performing translation based on the skeleton point of the signer. It contributed to performance improvement by a customized normalization method depending on the body parts. Our method can be applied to various datasets in a way that can be applied to datasets without glosses.
• Score: 7.240731862549344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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