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

This work dedicates to continuous sign language recognition (CSLR), which is a weakly
supervised task dealing with the recognition of continuous signs from videos, without any …

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 …

Sign languages are visual languages which convey information by signers' handshape,
facial expression, body movement, and so forth. Due to the inherent restriction of …

Abstract Machine learning for sign languages is bottlenecked by data. In this paper, we
present YouTube-ASL, a large-scale, open-domain corpus of American Sign Language …

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 …

Direct speech-to-speech translation (S2ST) with discrete units leverages recent progress in
speech representation learning. Specifically, a sequence of discrete representations derived …

Abstract Sign Language Translation (SLT) is a challenging task that aims to generate
spoken language sentences from sign language videos, both of which have different …

Audio-visual text generation aims to understand multi-modality contents and translate them
into texts. Although various transfer learning techniques of text generation have been …