Pronunciation error detection for computer-assisted language learning system based on error rule clustering using a decision tree

A pronunciation error detection method based on pronunciation error clustering is proposed for computer-assisted language learning (CALL) systems. The method uses a decision-tree-based clustering algorithm, which automatically generates a decision tree from a large number of speech samples, in the mispronunciation rules. The acoustic analysis is conducted by using English and Japanese hidden Markov models (HMM), both of them are gender-dependent monophones with single Gaussian distribution functions. The method, by using different threshold for each cluster, provides marked improvement in pronunciation error detection.