Abstract
We work on automatic Japanese sign Language (JSL) recognition using Hidden Markov Model (HMM). An important issue for modeling sign is that how to determine the constituent element of sign (i.e., subunit) like phoneme in spoken language. We focused on special feature of sign language that JSL is composed of three types of phonological elements which is hand local information, position, and movement. In this paper, we propose an efficiently method of generating subunit using multi-stream HMM which is correspond to phonological elements. An isolated word recognition experiment has confirmed the effectiveness of our proposed method.
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