In this paper, we proposed a novel method for evaluating intonation of an English utterance spoken by a learner for intonation learning by a CALL system. The proposed method is based on an intonation evaluation method proposed by Suzuki et al., which uses “word importance factors,” which are calculated based on word clusters given by a decision tree. We extended Suzuki’s method so that multiple decision trees are used and the resulting intonation scores are combined using multiple regression. As a result of an experiment, we obtained correlation coefficient comparable to the correlation between human raters.