Automatic detection of common mispronunciations of Vietnamese speakers of English using SVMs
Thanh-Dung Dang, Kim-Giao Dang Thi · 2017
Pronunciation errors are often made by language learners. Especially, systematic mispronunciations, consisting of substitutions of native sounds for sounds of the target language that do not exist in the native language, are considered a big problem for language leaners. Therefore, automatic detection of this kind of errors is essential to building a Computer-Assisted Language Learning (CALL) system supporting language learners to improve their pronunciation. In this research, we focused on detecting systematic pronunciation errors made by Vietnamese learners of English. To this end, we used SVM classifiers, which are trained by a native corpuses (TIMIT) and a non-native corpus (V.E Corpus). The non-native corpus, constructed by the researchers and annotated by two Vietnamese trained professionals, includes 1550 utterances from 31 Vietnamese students. Each of the students was asked to read 50 English sentences designed to contain English phonemes frequently mispronounced by Vietnamese speakers. The experimental results showed that the detectors can achieve at least 79% SAR and 10% FAR.