Motion analysis of grammatical processes in a visual-gestural language
Proc. of the ACM SIGGRAPH/SIGART interdisciplinary workshop on Motion: representation and perception
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
A Method for Analyzing Spatial Relationships Between Words in Sign Language Recognition
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
ARGo: An Architecture for Sign Language Recognition and Interpretation
Proceedings of Gesture Workshop on Progress in Gestural Interaction
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
American sign language recognition: reducing the complexity of the task with phoneme-based modeling and parallel hidden markov models
Understanding gestures with systematic variations in movement dynamics
Pattern Recognition
Asynchrony modeling for audio-visual speech recognition
HLT '02 Proceedings of the second international conference on Human Language Technology Research
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This paper addresses an aspect of sign language (SL) recognition that has largely been overlooked in previous work and yet is integral to signed communication. It is the most comprehensive work to-date on recognizing complex variations in sign appearances due to grammatical processes (inflections) which systematically modulate the temporal and spatial dimensions of a root sign word to convey information in addition to lexical meaning. We propose a novel dynamic Bayesian network - the Multichannel Hierarchical Hidden Markov Model (MH-HMM)- as a modelling and recognition framework for continuously signed sentences that include modulated signs. This models the hierarchical, sequential and parallel organization in signing while requiring synchronization between parallel data streams at sign boundaries. Experimental results using particle filtering for decoding demonstrate the feasibility of using the MH-HMM for recognizing inflected signs in continuous sentences.