Automatic translation from signed to spoken languages is an interdisciplinary research
domain on the intersection of computer vision, machine translation (MT), and linguistics …

Most sign language translation (SLT) methods to date require the use of gloss annotations to
provide additional supervision information, however, the acquisition of gloss is not easy. To …

The advances in automatic sign language translation (SLT) to spoken languages have been
mostly benchmarked with datasets of limited size and restricted domains. Our work …

Sign language is the main communication way for deaf and hard-of-hearing (ie, DHH)
people, which is unfamiliar to most non-deaf and hard-of-hearing (non-DHH) people. To …

Sign language translation (SLT) aims to convert a continuous sign language video clip into a
spoken language. Considering different geographic regions generally have their own native …

Most of the research to date focuses on bilingual sign language translation (BSLT).
However, such models are inefficient in building multilingual sign language translation …

Existing work on sign language translation-that is, translation from sign language videos into
sentences in a written language-has focused mainly on (1) data collected in a controlled …

There are two problems that widely exist in current end-to-end sign language processing
architecture. One is the CTC spike phenomenon which weakens the visual representational …

Word-level sign language recognition (WSLR) is the backbone for continuous sign language
recognition (CSLR) that infers glosses from sign videos. Finding the relevant gloss from the …

While machine translation for spoken language has advanced significantly, research on sign
language translation (SLT) for deaf individuals remains limited. Obtaining annotations, such …