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

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.