Automatic mispronunciation detection for english learners by GMM-UBM and GLDS-SVM methods

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 stateconcatenated 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,GLDSSVM processes a higher computation speed and smaller model size than traditional radial basic function (RBF) kernel.