Adding American Sign Language (ASL) animation to websites can improve information access for people who are deaf with low levels of English literacy. Given a script representing the sequence of ASL signs, we must generate an animation, but a challenge is selecting accurate speed and timing for the resulting animation. In this work, we analyzed motion-capture data recorded from human ASL signers to model the realistic timing of ASL movements, with a focus on where to insert prosodic breaks (pauses), based on the sentence syntax and other features. Our methodology includes extracting data from a pre-existing ASL corpus at our lab, selecting suitable features, and building machine learning models to predict where to insert pauses. We evaluated our model using cross-validation and compared various subsets of features. Our model had 80% accuracy at predicting pause locations, out-performing a baseline model on this task.
@InProceedings{AL-KHAZRAJI18.18013, author = {Sedeeq Al-khazraji ,Sushant Kafle and Matt Huenerfauth}, title = {Modeling and Predicting the Location of Pauses for the Generation of Animations of American Sign Language}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Mayumi Bono and Eleni Efthimiou and
Stavroula-Evita Fotinea and Thomas Hanke and
Julie Hochgesang and Jette Kristoffersen and
Johanna Mesch and Yutaka Osugi}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-01-6}, language = {english} }
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