In our approach, we first collect a competing token set for each physical HMM from training data. An off-line token collection procedure is used in this work to collect the competing-tokens from word lattices. Then we re-estimate HMM parameters discriminatively to minimize the total number of competing tokens counted in the phone level. The phone token counts are approximated by a sigmoid-based objective function. The GPD algorithm is used to adjust HMM parameters to minimize the objective function. In this work, a merging mechanism and a gradient normalization in the HMM tied-state level are proposed to improve the generalization power of our discriminative training method. The proposed method is evaluated on the resource management (RM) and the switchboard (a 24-hr mini-train set) tasks. Experimental results clearly show that our new discriminative training method achieves significant improvements over our best MLE models in both tasks, namely about 8% and 4.5% relative error rate reduction in RM and switchboard respectively, over the best MLE models.