The speech medium is more than an audio conveyance of word strings. It contains meta information about the content of the speech. The prosody of speech, pauses and intonation, adds an extra dimension of diagnostic information about the quality of a speaker\u27s answers, suggesting an important avenue of research for spoken dialogue tutoring systems. Tutoring systems that are sensitive to such cues may employ different tutoring strategies based on detected student uncertainty, and they may be able to perform more precise assessment of the area of student difficulty. However, properly identifying the cues can be challenging, typically requiring thousands of hand labeled utterances for training in machine learning. This study proposes and explores means of exploiting alternate automatically generated information, utterance correctness and the amount of practice a student has had, as indicators of student uncertainty. It finds correlations with various prosodic features and these automatic indicators and compares the result with a small set of annotated utterances, and finally demonstrates a Bayesian classifier based on correctness scores as class labels
