Training a confidence measure for a reading tutor that listens

One issue in a Reading Tutor that listens is to determine which words the student read correctly. We describe a confidence measure that uses a variety of features to estimate the probability that a word was read correctly. We trained two decision tree classifiers. The first classifier tries to fix insertion and substitution errors made by the speech decoder, while the second classifier tries to fix deletion errors. By applying the two classifiers together, we achieved a relative reduction in false alarm rate by 25.89% while holding the miscue detection rate constant.