The Colorado Literacy Tutor (CLT) is a technology-based literacy program, designed on the basis of cognitive theory and scientifically motivated reading research, which aims to improve literacy and student achievement in public schools. One of the critical components of the CLT is a speech recognition system which is used to track the child's progress during oral reading and to provide sufficient information to detect reading miscues. In this paper, we extend on prior work by examining a novel labeling of children's oral reading audio data in order to better understand the factors that contribute most significantly to speech recognition errors. While these events make up nearly 8% of the data, they are shown to account for approximately 30% of the word errors in a state-of-the-art speech recognizer. Next, we consider the problem of detecting miscues during oral reading. Using features derived from the speech recognizer, we demonstrate that 67% of reading miscues can be detected at a false alarm rate of 3%.
['Katherine J. Lee', 'Andreas Hagen', 'Nicholas Romanyshyn', 'Sean Martin', 'Bryan L. Pellom']
Analysis and detection of reading miscues for interactive literacy tutors