Visual sign language recognition based on HMMs and auto-regressive HMMs

Authors:
Xiaolin Yang;Feng Jiang;Han Liu;Hongxun Yao;Wen Gao;Chunli Wang
Affiliations:
Department of Computer Science, Harbin Institute of Technology, Harbin, China;Department of Computer Science, Harbin Institute of Technology, Harbin, China;Department of Computer Science, Univerity of Illinois at Urbana-Champaign, Champaign, IL;Department of Computer Science, Harbin Institute of Technology, Harbin, China;Department of Computer Science, Harbin Institute of Technology, Harbin, China;Institute of Computing Technology, Chinese Academy of Science, Beijing, China
Venue:
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Year:
2005

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Abstract

A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don't have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is proposed for sign language recognition. The corresponding training and recognition algorithms for ARHMM are also developed. A hybrid structure to combine ARHMMs with HMMs based on the trick of using an ambiguous word set is presented and the advantages of both models are revealed in such a frame work.