Abstract: The paper proposes an efficient generalized linear discriminant sequence based SVM (GLDS-SVM) based mispronunciation detection method. Firstly, in order to enhance the ability of describing pronunciation characteristics, we introduce an improved SVM feature normalization scheme based on state-concatenated operation. Then, we propose a novel multi-model strategy for model training to make full use of samples and solve the problem of data unbalance caused by lack of the actual mispronunciation corpus. Finally, we combine GLDS-SVM with universal background models based GMM (GMM-UBM) to further improve the performance. The fused system by these two methods achieves 9.92% and 16.35% in equal error rate (EER) for simulation set and real set, respectively. Meanwhile, GLDS-SVM processes a higher computation speed and smaller model size than traditional radial basic function (RBF) kernel.