Advancements in AI will soon enable tools for providing automatic feedback to American Sign Language (ASL) learners on some aspects of their signing, but there is a need to understand their preferences for submitting videos and receiving feedback. Ten participants in our study were asked to record a few sentences in ASL using software we designed, and we provided manually curated feedback on one sentence in a manner that simulates the output of a future automatic feedback system. Participants responded to interview questions and a questionnaire eliciting their impressions of the prototype. Our initial findings provide guidance to future designers of automatic feedback systems for ASL learners.

To improve computer-based recognition from video of isolated signs from American Sign Language (ASL), we propose a new skeleton-based method that involves explicit detection of the start and end frames of signs, trained on the ASLLVD dataset; it uses linguistically relevant parameters based on the skeleton input. Our method employs a bidirectional learning approach within a Graph Convolutional Network (GCN) framework. We apply this method to the WLASL dataset, but with corrections to the gloss labeling to ensure consistency in the labels assigned to different signs; it is important to have a 1-1 correspondence between signs and text-based gloss labels. We achieve a success rate of 77.43% for top-1 and 94.54% for top-5 using this modified WLASL dataset. Our method, which does not require multi-modal data input, outperforms other state-of-the-art approaches on the same modified WLASL dataset, demonstrating the importance of both attention to the start and end frames of signs and the use of bidirectional data streams in the GCNs for isolated sign recognition.

Without a commonly accepted writing system for American Sign Language (ASL), Deaf or Hard of Hearing (DHH) ASL signers who wish to express opinions or ask questions online must post a video of their signing, if they prefer not to use written English, a language in which they may feel less proficient. Since the face conveys essential linguistic meaning, the face cannot simply be removed from the video in order to preserve anonymity. Thus, DHH ASL signers cannot easily discuss sensitive, personal, or controversial topics in their primary language, limiting engagement in online debate or inquiries about health or legal issues. We explored several recent attempts to address this problem through development of “face swap” technologies to automatically disguise the face in videos while preserving essential facial expressions and natural human appearance. We presented several prototypes to DHH ASL signers (N=16) and examined their interests in and requirements for such technology. After viewing transformed videos of other signers and of themselves, participants evaluated the understandability, naturalness of appearance, and degree of anonymity protection of these technologies. Our study revealed users’ perception of key trade-offs among these three dimensions, factors that contribute to each, and their views on transformation options enabled by this technology, for use in various contexts. Our findings guide future designers of this technology and inform selection of applications and design features.

Despite the fact that there is critical grammatical information expressed through facial expressions and head gestures, most research in the field of sign language recognition has primarily focused on the manual component of signing.We propose a novel framework for robust tracking and analysis of non-manual behaviours, with an application to sign language recognition.The novelty of our method is threefold.First, we propose a dynamic feature representation.Instead of using only the features available in the current frame (e.g., head pose), we additionally aggregate and encode the feature values in neighbouring frames to better encode the dynamics of expressions and gestures (e.g., head shakes).Second, we use Multiple Instance Learning [12] to handle feature misalignment resulting from drifting of the face tracker and partial occlusions.Third, we utilize a discriminative Hidden Markov Support Vector Machine (HMSVM) [1] to learn finer temporal dependencies between the features of interest.We apply our signerindependent framework to segmented recognition of five classes of grammatical constructions conveyed through facial expressions and head gestures: wh-questions, negation, conditional/when clauses, yes/no questions and topics, and show improvement over previous methods.

Handshape is a key linguistic component of signs, and thus, handshape recognition is essential to algorithms for sign language recognition and retrieval. In this work, linguistic constraints on the relationship between start and end handshapes are leveraged to improve handshape recognition accuracy. A Bayesian network formulation is proposed for learning and exploiting these constraints, while taking into consideration inter-signer variations in the production of particular handshapes. A Variational Bayes formulation is employed for supervised learning of the model parameters. A non-rigid image alignment algorithm, which yields improved robustness to variability in handshape appearance, is proposed for computing image observation likelihoods in the model. The resulting handshape inference algorithm is evaluated using a dataset of 1500 lexical signs in American Sign Language (ASL), where each lexical sign is produced by three native ASL signers.

Given that sign language is used as a primary means of communication by as many as two million deaf individuals in the U.S. and as augmentative communication by hearing individuals with a variety of disabilities, the development of robust, real-time sign language recognition technologies would be a major step forward in making computers equally accessible to everyone. However, most research in the field of sign language recognition has focused on the manual component of signs, despite the fact that there is critical grammatical information expressed through facial expressions and head gestures.We propose a novel framework for robust tracking and analysis of facial expression and head gestures, with an application to sign language recognition. We then apply it to recognition with excellent accuracy (≥=95%) of two classes of grammatical expressions, namely wh-questions and negative expressions. Our method is signer-independent and builds on the popular bag-of-words model, utilizing spatial pyramids to model facial appearance and temporal pyramids to represent patterns of head pose changes.