An efficient context-free parsing algorithm
Communications of the ACM
Computational Linguistics
The MathSigner: An Interactive Learning Tool for American Sign Language K-3 Mathematics
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Charting the depths of robust speech parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
An Adaptive WWW-Based System to Teach British Sign Language
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
An immersive virtual environment for learning sign language mathematics
ACM SIGGRAPH 2006 Educators program
Educational resources and implementation of a Greek sign language synthesis architecture
Computers & Education
Providing signed content on the Internet by synthesized animation
ACM Transactions on Computer-Human Interaction (TOCHI)
Inputted text to animated sign language, interactive interface, a self-learning with fun
ACM SIGGRAPH 2007 educators program
Prototype machine translation system from text-to-Indian sign language
Proceedings of the 13th international conference on Intelligent user interfaces
Speech to sign language translation system for Spanish
Speech Communication
SignTutor: An Interactive System for Sign Language Tutoring
IEEE MultiMedia
Partial parse selection for robust deep processing
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Hybrid paradigm for Spanish Sign Language synthesis
Universal Access in the Information Society
A rule-based translation from written Spanish to Spanish Sign Language glosses
Computer Speech and Language
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This paper presents the first results of the integration of a Spanish-to-LSE Machine Translation (MT) system into an e-learning platform. Most elearning platforms provide speech-based contents, which makes them inaccessible to the Deaf. To solve this issue, we have developed a MT system that translates Spanish speech-based contents into LSE. To test our MT system, we have integrated it into an e-learning tool. The elearning tool sends the audio to our platform. The platform sends back the subtitles and a video stream with the signed translation to the e-learning tool. Preliminary results, evaluating the sign language synthesis module, show an isolated sign recognition accuracy of 97%. The sentence recognition accuracy was of 93%.