An automatic pitch accent feedback system for english learners with adaptation of an english corpus spoken by Koreans

Abstract

To improve the English proficiency of Korean learners, we design a system for pitch accents, which consists of prediction, detection and feedback parts. The prediction and detection parts adopt Conditional Random Field models to achieve a prediction accuracy of 87.25%, which is based on the Boston University radio news corpus, and a detection accuracy of 81.21%, which is based on the Korean Learner's English Accentuation corpus. In the learner experiment with our system, learners' pitch accent proficiency, as assessed by English experts, was improved from 2.67 to 3.25 on a scale of 1-to-5, and the accuracy of not-wrong feedback was measured at 82.77%. The learners assessed the learning effectiveness of our system at 4.3 on a scale of 1-to-5.