Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

Improving Spatial Reference in American Sign Language Animation through Data Collection from Native ASL Signers

Abstract. Many deaf adults in the U.S. have difficulty reading written English text; computer animations of American Sign Language (ASL) can improve these individuals ' access to information, communication, and services. Current ASL animation technology cannot automatically generate expressions in which the signer associates locations in space with entities under discussion, nor can it generate many ASL signs whose movements are modified based on these locations. To determine how important such phenomena are to user-satisfaction and the comprehension of animations by deaf individuals, we conducted a study in which native ASL signers evaluated ASL animations with and without entity-representing spatial phenomena. We found that the inclusion of these expressions in the repertoire of ASL animation systems led to a significant improvement in user comprehension of the animations, thereby motivating future research on automatically generating such ASL spatial expressions