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

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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.

Associated lab/group: Project LISTEN
Associated project: Project LISTEN's Reading Tutor

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

@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"
}