Time: 2pm-3pm, Friday, December 4, 2009 Place: Room 4422, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Speaker: Matt Huenerfauth (CUNY) Title: A Motion-Capture Corpus of American Sign Language for Generation Research Abstract: A majority of deaf 18-year-olds in the United States have a fourth-grade English reading level or below. Software that can present information in the form of American Sign Language (ASL) animations or automatically translate English text to ASL could significantly improve these individuals' access to information, communication, and services. ASL is a natural language with a distinct grammar and vocabulary from English; so, computational linguistic tools for generating grammatically correct and understandable ASL sentences (to be performed by a virtual animated human character) must be developed. The motion-path of individual signs in an ASL sentence can vary greatly, depending on various linguistic factors. For instance, entities under discussion can be associated with 3D points in space around a signer, and the movements of verb signs are deflected from their standard motion path based on how the subject and object of the verb have been "set up" in the signing space. Computational models of the motion path of signs and the use of space by signers are necessary for generating natural and understandable ASL sentences -- concatenation of animations of signs from a fixed lexicon is insufficient for generating correctly inflected signs or natural coarticulation effects. For this reason, CUNY has begun a multi-year project to build the first motion-capture corpus of multi-sentential ASL utterances. Native ASL signers are being recorded performing spontaneous and directed ASL sentences while wearing motion-capture body suits, gloves, eye-trackers, and head-trackers. This data is being linguistically annotated with syntactic and discourse information by native ASL signers to produce a permanent research resource. As an initial use of this corpus, we are studying how to learn spatially-parameterized models of ASL verb signs so that our ASL animation technology can synthesize novel ASL verb performances for unseen arrangements of subject/object reference points in the signing space. This would be a necessary component of a fluent ASL generation system. This talk will give an overview of the project, our corpus collection and annotation techniques, our user-based ASL animation evaluation approach, and our current progress. Speaker Bio: Matt Huenerfauth is an assistant professor of Computer Science at CUNY Graduate Center and CUNY Queens College. His research focuses on the design of computer technology to benefit people who are deaf or have low levels of written-language literacy. His work is at the intersection of the fields of assistive technology for people with disabilities, computational linguistics, virtual human animation, and the linguistics of American Sign Language (ASL). In 2005 and 2007, he received the Best Paper Award at the ACM SIGACCESS Conference on Computers and Accessibility, the major computer science conference on assistive technology for people with disabilities. In 2008, he received a five-year Faculty Early Career Development (CAREER) Award from the National Science Foundation to support his research on ASL. In 2008, he became an Associate Editor of the ACM Transactions on Accessible Computing (TACCESS), the Association of Computing Machinery's journal in the field of assistive technology and accessibility for people with disabilities.