The recognition of large vocabulary continuous Chinese Sign Language (CSL) is a challenging problem. It is effective to use phonemes instead of whole signs as the basic units. In this paper, an approach to extracting the basic units in CSL automatically is described. In order to find subwords in each data streams in sign signals, Dynmaic Programming (DP) is proposed to segment the data streams, and then ANN approach combining k-means is used to classify these segments. 71 hand postures are automatically extracted from 1063 words and 200 continuous sentences. These postures accompanied with locations and orientations will be used as basic units in large vocabulary continuous CSL recognition.
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