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Carnegie Mellon Robotics Institute

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Abstract

This paper explores the problem of predicting specific reading mistakes, called miscues, on a given word. Characterizing likely miscues tells an automated reading tutor what to anticipate, detect, and remediate. As training and test data, we use a database of over 100,000 miscues transcribed by University of Colorado researchers. We explore approaches that exploit different sources of predictive power: the uneven distribution of words in text, and the fact that most miscues are real words. We compare the approaches' ability to predict miscues of other readers on other text. A simple rote method does best on the most frequent 100 words of English, while an extrapolative method for predicting real-word miscues performs well on less frequent words, including words not in the training data.

Notes

Associated Lab(s) / Group(s): Project LISTEN

Associated Project(s): Project LISTEN's Reading Tutor

Text Reference

Jack Mostow, Joseph E. Beck, Sylvia V. Winter, S. Wang, and Brian Tobin, "Predicting oral reading miscues," Seventh International Conference on Spoken Language Processing (ICSLP-02), September, 2002.

BibTeX Reference

@inproceedings{Mostow_2002_4067,
   author = "Jack Mostow and Joseph E Beck and Sylvia V. Winter and S. Wang and Brian Tobin",
   title = "Predicting oral reading miscues",
   booktitle = "Seventh International Conference on Spoken Language Processing (ICSLP-02)",
   month = "September",
   year = "2002",
}

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