Sign language processing is the field of research that aims to recognize, retrieve, and spot signs in videos. Various approaches have been developed, varying in whether they use linguistic features and whether they use landmark detection tools or not. Incorporating linguistics holds promise for improving sign language processing in terms of performance, generalizability, and explainability. This paper focuses on the task of sign spotting and aims to expand on the approximative linguistic features that have been used in previous work, and to understand when linguistic features deliver an improvement over landmark features. We detect landmarks with Mediapipe and extract linguistically relevant features from them, including handshape, orientation, location, and movement. We compare a sign spotting model using linguistic features with a model operating on landmarks directly, finding that the approximate linguistic features tested in this paper capture some aspects of signs better than the landmark features, while they are worse for others.

We introduce a cross-linguistic database for attitude predicates, which references their combinatorial (syntactic) and semantic properties. Our data allows assessment of cross-linguistic generalizations about attitude predicates as well as discovery of new typological/cross-linguistic patterns. This paper motivates empirical and theoretical issues that our database will help to address, the sample predicates and the properties that it references, as well as our design and methodological choices. Two case studies illustrate how the database can be used to assess validity of cross-linguistic generalizations.

Prior studies on spoken languages have shown that indefinite and interrogative pronouns may be formally very similar. Our research aims to understand if sign languages exhibit this type of affinity. This paper presents an overview of the phenomenon and reports on the results of two studies: a cross-linguistic survey based on a sample of 30 sign languages and an empirical investigation conducted with three deaf consultants of Catalan Sign Language (LSC). Our research shows that, in sign languages, certain signs have both existential and interrogative readings and it identifies the environments that make existential interpretations available in LSC.

This paper presents first steps towards a sign language avatar for communicating railway travel announcements in Dutch Sign Language. Taking an interdisciplinary approach, it demonstrates effective ways to employ co-design and focus group methods in the context of developing sign language technology, and presents several concrete findings and results obtained through co-design and focus group sessions which have not only led to improvements of our own prototype but may also inform the development of signing avatars for other languages and in other application domains.

We present a number of methodological recommendations concerning the online evaluation of avatars for text-to-sign translation, focusing on the structure, format and length of the questionnaire, as well as methods for eliciting and faithfully transcribing responses

Communication between healthcare professionals and deaf patients is challenging, and the current COVID-19 pandemic makes this issue even more acute. Sign language interpreters can often not enter hospitals and face masks make lipreading impossible. To address this urgent problem, we developed a system which allows healthcare professionals to translate sentences that are frequently used in the diagnosis and treatment of COVID-19 into Sign Language of the Netherlands (NGT). Translations are displayed by means of videos and avatar animations. The architecture of the system is such that it could be extended to other applications and other sign languages in a relatively straightforward way.