Abstract: A complementary system generation method based on confusing information weighting is proposed within the framework of discriminative training.Firstly,each pair of confusing phones is dynamically weighted according to the phone confusion information,and the weighted phone accuracy is calculated by referring to the three best hypothesis paths of the base system.Meanwhile,the standard phone accuracy is obtained using the true transcription as the reference.Then,a model space complementary system is constructed by maximizing the weighted phone accuracy,and by minimizing the standard phone accuracy simultaneously.Furthermore,through combining the model-space complementary system-generating method with the RDLT feature transform process,a feature space complementary system is constructed.Experimental results show that compared with the complementary minimum phone error criterion,the recognition rate is increased by 0.76%by combining the base system with the model space complementary system.The performance gain is increased to 1.35%when combining the base system with both the feature and model space complementary systems.The presented method can enlarge the diversity among the complementary systems and improve the recognition rate of the combined system.