Self-organized language modeling for speech recognition
Readings in speech recognition
Statistical methods for speech recognition
Statistical methods for speech recognition
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
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FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Viewpoint invariant sign language recognition
Computer Vision and Image Understanding
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Person-Independent 3D Sign Language Recognition
Gesture-Based Human-Computer Interaction and Simulation
Modelling and segmenting subunits for sign language recognition based on hand motion analysis
Pattern Recognition Letters
Video-based signer-independent Arabic sign language recognition using hidden Markov models
Applied Soft Computing
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ICCS 2009 Proceedings of the 9th International Conference on Computational Science
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A framework for continuous multimodal sign language recognition
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A Chinese sign language recognition system based on SOFM/SRN/HMM
Pattern Recognition
Novel boosting framework for subunit-based sign language recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A database-based framework for gesture recognition
Personal and Ubiquitous Computing
Transition movement models for large vocabulary continuous sign language recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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The Journal of Machine Learning Research
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Artificial Intelligence Review
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This paper is concerned with the automatic recognition of German continuous sign language. For the most user-friendliness only one single color video camera is used for image recording. The statistical approach is based on the Bayes decision rule for minimum error rate. Following speech recognition system design, which are in general based on subunits, here the idea of an automatic sign language recognition system using subunits rather than models for whole signs will be outlined. The advantage of such a system will be a future reduction of necessary training material. Furthermore, a simplified enlargement of the existing vocabulary is expected. Since it is difficult to define subunits for sign language, this approach employs totally self-organized subunits called fenone. K-means algorithm is used for the definition of such fenones. The software prototype of the system is currently evaluated in experiments.