Landmark-based automated pronunciation error detection

We present a pronunciation error detection method for second language learners of English (L2 learners). The method is a combination of confidence scoring and landmark-based Support Vector Machines (SVMs). Landmark-based SVMs were implemented to specialize the method for the specific phonemes with which L2 learners make frequent errors. The method was trained for the difficult phonemes for Korean learners and tested on intermediate Korean learners. In the data where distortion errors (non-phonemic errors) occupied high proportion, SVM method achieved significantly higher Fscore (0.67) than confidence scoring (0.60). However, the combination of two methods without the appropriate training data did not lead to improvement. Even for intermediate learners, a high proportion of errors (40%) was related to these difficult phonemes. Therefore, the method specialized for these phonemes will be beneficial for both beginners and intermediate learners.