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The universal structure of speech [1, 2] , proves to be invariant to transformations in feature space, and thus provides a robust representation for speech recognition. One of the difficulties of using structure representation is due to its high dimensionality. This not only increases computational cost but also easily suffers from the curse of dimensionality [3, 4] . In this paper, we introduce Random Discriminant Structure Analysis (RDSA) to deal with this problem. Based on the observationdoi:10.1109/asru.2007.4430176 dblp:conf/asru/QiaoAM07 fatcat:y4a47jf6zvhaxl46ylrlk7bw24