Decision tree based tone modeling with corrective feedbacks for automatic Mandarin tone assessment

We propose a novel decision tree based approach to Mandarin tone assessment. In most conventional computer assisted pronunciation training (CAPT) scenarios a tone production template is prepared as a reference with only numeric scores as feedbacks for tone learning. In contrast decision trees trained with an annotated tone-balanced corpus make use of a collection of questions related to important cues in categories of tone production. By traversing the corresponding paths and nodes associated with a test utterance a sequence of corrective comments can be generated to guide the learner for potential improvement. Therefore a detailed pronunciation indication or a comparison between two paths can be provided to learners which are usually unavailable in score-based CAPT systems.