The universal speech attributes to speaker verification (SV) is addressed in this paper. The manner and place of articulation form the universal attribute unit inventory, and deep neural network (DNN) is used as acoustic model. Two methods to generate the attribute units are proposed in this paper: one is that the manner and place of articulation are directly combined to generate more robust universal speech attribute units, and the other is that the different context-dependent speech arttribute units are merged to a new speech attribute unit set by means of automatic clustering in accordance with likelihood calculation. Evaluated on the core test from the 2008 NIST speaker verification evaluation (SRE), the novel generated attribute units based system can achieve a better performance than that of the phoneme based system. Furthermore, the attribute based system has demonstrated a good complementarity with the GMM-UBM/i-vector and phoneme based DNN/i-vector systems