An efficient mispronounciation detction method using GLDS-SVM and formant enhanced features

Mispronunciation detection is an important component in computer assisted language learning (CALL) system. In this work, we introduce an efficient GLDS-SVM based detection method, which is successfully used in language and speaker identification systems, and combine it with traditional methods. The main ideas include: extended MFCC features with normalized formant trajectory information, and then propose a novel multi-model strategy for model training to make full use of samples and solve the problem of data unbalance, finally combine GLDS-SVM method with UBM-GMM system to further improve the performance. Experiments show that GLDS-SVM is highly efficient than traditional RBF-SVM, and the fused system can achieve a significant relative improvement of 17.5% in EER reduction, compared with the baseline UBM-GMM system.