SIGACCESS

Rochester Institute of Technology, Rochester, NY, USA

To enable more websites to provide content in the form of sign language, we investigate software to partially automate the synthesis of animations of American Sign Language (ASL), based on a human-authored message specification. We automatically select: where prosodic pauses should be inserted (based on the syntax or other features), the time-duration of these pauses, and the variations of the speed at which individual words are performed (e.g. slower at the end of phrases). Based on an analysis of a corpus of multi-sentence ASL recordings with motion-capture data, we trained machine-learning models, which were evaluated in a cross-validation study. The best model out-performed a prior state-of-the-art ASL timing model. In a study with native ASL signers evaluating animations generated from either our new model or from a simple baseline (uniform speed and no pauses), participants indicated a preference for speed and pausing in ASL animations from our model.