Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Sign2Gloss2Text-based Sign Language Translation with Enhanced Spatial-temporal Information Centered on Sign Language Movement Keypoints

수어 동작 키포인트 중심의 시공간적 정보를 강화한 Sign2Gloss2Text 기반의 수어 번역

  • Kim, Minchae (Graduate School of Information, Yonsei University) ;
  • Kim, Jungeun (Dept. of Artificial Intelligence, Graduate School, Yonsei University) ;
  • Kim, Ha Young (Graduate School of Information, Yonsei University)
  • Received : 2022.09.29
  • Accepted : 2022.10.17
  • Published : 2022.10.31
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Abstract

Sign language has completely different meaning depending on the direction of the hand or the change of facial expression even with the same gesture. In this respect, it is crucial to capture the spatial-temporal structure information of each movement. However, sign language translation studies based on Sign2Gloss2Text only convey comprehensive spatial-temporal information about the entire sign language movement. Consequently, detailed information (facial expression, gestures, and etc.) of each movement that is important for sign language translation is not emphasized. Accordingly, in this paper, we propose Spatial-temporal Keypoints Centered Sign2Gloss2Text Translation, named STKC-Sign2 Gloss2Text, to supplement the sequential and semantic information of keypoints which are the core of recognizing and translating sign language. STKC-Sign2Gloss2Text consists of two steps, Spatial Keypoints Embedding, which extracts 121 major keypoints from each image, and Temporal Keypoints Embedding, which emphasizes sequential information using Bi-GRU for extracted keypoints of sign language. The proposed model outperformed all Bilingual Evaluation Understudy(BLEU) scores in Development(DEV) and Testing(TEST) than Sign2Gloss2Text as the baseline, and in particular, it proved the effectiveness of the proposed methodology by achieving 23.19, an improvement of 1.87 based on TEST BLEU-4.

Keywords

Acknowledgement

This research was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT; Ministry of Science and ICT) (2020R1F1A1071527).

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