Action-Concentrated Embedding Framework: This is your captain sign-tokening
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, Hyunwook | - |
dc.contributor.author | Shin, Suhyeon | - |
dc.contributor.author | Heo, Jungu | - |
dc.contributor.author | Shin, Hyuntaek | - |
dc.contributor.author | Kim, Hyosu | - |
dc.contributor.author | Kim, Mucheol | - |
dc.date.accessioned | 2024-07-02T06:30:45Z | - |
dc.date.available | 2024-07-02T06:30:45Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/74528 | - |
dc.description.abstract | Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign token embedding framework. Additionally, to provide a more structured foundation for sign language analysis, we introduce a dedicated notation system tailored for sign language that endeavors to encapsulate the nuanced gestures and movements that are integral with sign communication. The proposed ACE approach tracks a signer's actions based on human posture estimation. Tokenizing these actions and capturing the token embedding using a short-time Fourier transform encapsulates the time-based behavioral changes. Hence, ACE offers input embedding to translate sign language into natural language sentences. When tested against a disaster sign language dataset using automated machine translation measures, ACE notably surpasses prior research in terms of translation capabilities, improving the performance by up to 5.79% for BLEU-4 and 5.46% for ROUGE-L metric. © 2024 ELRA Language Resource Association: CC BY-NC 4.0. | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | European Language Resources Association (ELRA) | - |
dc.title | Action-Concentrated Embedding Framework: This is your captain sign-tokening | - |
dc.type | Article | - |
dc.identifier.bibliographicCitation | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, pp 310 - 320 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85195986444 | - |
dc.citation.endPage | 320 | - |
dc.citation.startPage | 310 | - |
dc.citation.title | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings | - |
dc.type.docType | Conference paper | - |
dc.subject.keywordAuthor | short-time Fourier transform | - |
dc.subject.keywordAuthor | sign language token embedding framework | - |
dc.subject.keywordAuthor | sign language translation | - |
dc.description.journalRegisteredClass | scopus | - |
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