Mining Free-form Spoken Responses to Tutor Prompts

How can an automated tutor assess children's spoken responses despite imperfect speech recognition? We address this challenge in the context of tutoring children in explicit strategies for reading comprehension. We report initial progress on collecting, annotating, and mining their spoken responses. Collection and annotation yield authentic but sparse data, which we use to synthesize additional realistic data. We train and evaluate a classifier to estimate the probability that a response mentions a given target.