정보처리학회 논문지 (The Transactions of the Korea Information Processing Society)

한국정보처리학회 (Korea Information Processing Society)
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수어 번역을 위한 3차원 컨볼루션 비전 트랜스포머

Three-Dimensional Convolutional Vision Transformer for Sign Language Translation

  • 성호렬 (고려대학교 컴퓨터정보학과) ;
  • 조현중 (고려대학교 컴퓨터융합소프트웨어학과 )
  • 투고 : 2023.11.22
  • 심사 : 2024.02.09
  • 발행 : 2024.03.31
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초록

한국에서 청각장애인은 지체장애인에 이어 두 번째로 많은 등록 장애인 그룹이다. 하지만 수어 기계 번역은 시장 성장성이 작고, 엄밀하게 주석처리가 된 데이터 세트가 부족해 발전 속도가 더디다. 한편, 최근 컴퓨터 비전과 패턴 인식 분야에서 트랜스포머를 사용한 모델이 많이 제안되고 있는데, 트랜스포머를 이용한 모델은 동작 인식, 비디오 분류 등의 분야에서 높은 성능을 보여오고 있다. 이에 따라 수어 기계 번역 분야에서도 트랜스포머를 도입하여 성능을 개선하려는 시도들이 제안되고 있다. 본 논문에서는 수어 번역을 위한 인식 부분을 트랜스포머와 3D-CNN을 융합한 3D-CvT를 제안한다. 또, PHOENIX-Wether-2014T [1]를 이용한 실험을 통해 제안 모델은 기존 모델보다 적은 연산량으로도 비슷한 번역 성능을 보이는 효율적인 모델임을 실험적으로 증명하였다.

In the Republic of Korea, people with hearing impairments are the second-largest demographic within the registered disability community, following those with physical disabilities. Despite this demographic significance, research on sign language translation technology is limited due to several reasons including the limited market size and the lack of adequately annotated datasets. Despite the difficulties, a few researchers continue to improve the performacne of sign language translation technologies by employing the recent advance of deep learning, for example, the transformer architecture, as the transformer-based models have demonstrated noteworthy performance in tasks such as action recognition and video classification. This study focuses on enhancing the recognition performance of sign language translation by combining transformers with 3D-CNN. Through experimental evaluations using the PHOENIX-Wether-2014T dataset [1], we show that the proposed model exhibits comparable performance to existing models in terms of Floating Point Operations Per Second (FLOPs).

키워드

과제정보

본 연구는 2023년 정부(교육부)의 재원으로 한국연구재단 기초연구사업의 지원을 받아 수행된 연구임(No. NRF-2021R1F1A1049202).

참고문헌

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