Evaluating Prosody of Mandarin Speech for Language Learning

Abstract This paper proposes an approach to automatically evaluate the prosody of Chinese Mandarin speech for language learning. In this approach, we grade the appropriateness of prosody of speech units according to a model speech corpus from a teacher’s voice. To this end, we build two models, which are the prosody model and the scoring model. The prosody model that is built from the teacher’s speech predicts the reference prosody for the learning text. The scoring model compares the student’s prosody with the reference prosody and gives a prosody rating score. Both the prosody model and the scoring model are built using regression tree. To make the two prosodies comparable, we transform the student’s prosody into the teacher’s prosody space. To build the scoring model, we derive from the corpus a reference data set, in which prosody rating is associated with prosody parameters. During speech evaluation, the student’s prosody is first transformed into the teacher’s prosody space and then evaluated by the scoring model. Experiments show that our model works well for speech of new speakers.