HCI for the deaf community: developing human-like avatars for sign language synthesis

With ever increasing computing power and advances in 3D animation technologies it is no surprise that 3D avatars for sign language (SL) generation are advancing too. Traditionally these avatars have been driven by somewhat expensive and inflexible motion capture technologies and perhaps this is the reason avatars do not feature in all but a few user interfaces (UIs). SL synthesis is a competing technology that is less costly, more versatile and may prove to be the answer to the current lack of access for the Deaf in HCI. This paper outlines the current state of the art in SL synthesis for HCI and how we propose to advance this by improving avatar quality and realism with a view to ameliorating communication and computer interaction for the Deaf community as part of a wider localisation project

Building a sign language corpus for use in machine translation

In recent years data-driven methods of machine translation (MT) have overtaken rule-based approaches as the predominant means of automatically translating between languages. A pre-requisite for such an approach is a parallel corpus of the source and target languages. Technological developments in sign language (SL) capturing, analysis and processing tools now mean that SL corpora are becoming increasingly available. With transcription and language analysis tools being mainly designed and used for linguistic purposes, we describe the process of creating a multimedia parallel corpus specifically for the purposes of English to Irish Sign Language (ISL) MT. As part of our larger project on localisation, our research is focussed on developing assistive technology for patients with limited English in the domain of healthcare. Focussing on the first point of contact a patient has with a GP’s office, the medical secretary, we sought to develop a corpus from the dialogue between the two parties when scheduling an appointment. Throughout the development process we have created one parallel corpus in six different modalities from this initial dialogue. In this paper we discuss the multi-stage process of the development of this parallel corpus as individual and interdependent entities, both for our own MT purposes and their usefulness in the wider MT and SL research domains

The Role of Emotional and Facial Expression in Synthesised Sign Language Avatars

This thesis explores the role that underlying emotional facial expressions might have in regards to understandability in sign language avatars. Focusing specifically on Irish Sign Language (ISL), we examine the Deaf community’s requirement for a visual-gestural language as well as some linguistic attributes of ISL which we consider fundamental to this research. Unlike spoken language, visual-gestural languages such as ISL have no standard written representation. Given this, we compare current methods of written representation for signed languages as we consider: which, if any, is the most suitable transcription method for the medical receptionist dialogue corpus. A growing body of work is emerging from the field of sign language avatar synthesis. These works are now at a point where they can benefit greatly from introducing methods currently used in the field of humanoid animation and, more specifically, the application of morphs to represent facial expression. The hypothesis underpinning this research is: augmenting an existing avatar (eSIGN) with various combinations of the 7 widely accepted universal emotions identified by Ekman (1999) to deliver underlying facial expressions, will make that avatar more human-like. This research accepts as true that this is a factor in improving usability and understandability for ISL users. Using human evaluation methods (Huenerfauth, et al., 2008) the research compares an augmented set of avatar utterances against a baseline set with regards to 2 key areas: comprehension and naturalness of facial configuration. We outline our approach to the evaluation including our choice of ISL participants, interview environment, and evaluation methodology. Remarkably, the results of this manual evaluation show that there was very little difference between the comprehension scores of the baseline avatars and those augmented withEFEs. However, after comparing the comprehension results for the synthetic human avatar “Anna” against the caricature type avatar “Luna”, the synthetic human avatar Anna was the clear winner. The qualitative feedback allowed us an insight into why comprehension scores were not higher in each avatar and we feel that this feedback will be invaluable to the research community in the future development of sign language avatars. Other questions asked in the evaluation focused on sign language avatar technology in a more general manner. Significantly, participant feedback in regard to these questions indicates a rise in the level of literacy amongst Deaf adults as a result of mobile technology

ASL Champ!: A Virtual Reality Game with Deep-Learning Driven Sign Recognition

We developed an American Sign Language (ASL) learning platform in a Virtual Reality (VR) environment to facilitate immersive interaction and real-time feedback for ASL learners. We describe the first game to use an interactive teaching style in which users learn from a fluent signing avatar and the first implementation of ASL sign recognition using deep learning within the VR environment. Advanced motion-capture technology powers an expressive ASL teaching avatar within an immersive three-dimensional environment. The teacher demonstrates an ASL sign for an object, prompting the user to copy the sign. Upon the user's signing, a third-party plugin executes the sign recognition process alongside a deep learning model. Depending on the accuracy of a user's sign production, the avatar repeats the sign or introduces a new one. We gathered a 3D VR ASL dataset from fifteen diverse participants to power the sign recognition model. The proposed deep learning model's training, validation, and test accuracy are 90.12%, 89.37%, and 86.66%, respectively. The functional prototype can teach sign language vocabulary and be successfully adapted as an interactive ASL learning platform in VR.Comment: 36 pages, 9 figure

Emotional engineering of artificial representations of sign languages

The fascination and challenge of making an appropriate digital representation of sign language for a highly specialised and culturally rich community such as the Deaf, has brought about the development and production of several digital representations of sign language (DRSL). These range from pictorial depictions of sign language, filmed video recordings to animated avatars (virtual humans). However, issues relating to translating and representing sign language in the digital-domain and the effectiveness of various approaches, has divided the opinion of the target audience. As a result there is still no universally accepted digital representation of sign language. For systems to reach their full potential, researchers have postulated that further investigation is needed into the interaction and representational issues associated with the mapping of sign language into the digital domain. This dissertation contributes a novel approach that investigates the comparative effectiveness of digital representations of sign language within different information delivery contexts. The empirical studies presented have supported the characterisation of the prescribed properties of DRSL's that make it an effective communication system, which when defined by the Deaf community, was often referred to as "emotion". This has led to and supported the developed of the proposed design methodology for the "Emotional Engineering of Artificial Sign Languages", which forms the main contribution of this thesis