Abstract
We describe a system for recognizing both the isolated and continuous Chinese sign language (CSL) using two cybergloves and two 3SAPCE-position trackers as gesture input devices. To get robust gesture features, each joint-angle collected by cybergloves is normalized. The relative position and orientation of the left hand to those of the right hand are proposed as the signer position-independent features. To speed up the recognition process, fast match and frame prediction techniques are proposed. To tackle the epenthesis movement problem, context-dependent models are obtained by the dynamic programming (DP) technique. HMM are utilized to model basic word units. Then we describe training techniques of the bigram language model and the search algorithm used in our baseline system. The baseline system converts sentence level gestures into synthesis speech and gestures of a 3D virtual human synchronously. Experiments show that these techniques are efficient both in recognition speed and recognition performance.