Training a Confidence Measure for a Reading Tutor that Listens

Y.-C. Tam, Joseph E. Beck, Jack Mostow, and S. Banerjee
Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech 2003), October, 2003.


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Abstract
One issue in a Reading Tutor that listens is to determine which words the student read correctly. We describe a confidence measure that uses a variety of features to estimate the probability that a word was read correctly. We trained two decision tree classifiers. The first classifier tries to fix insertion and substitution errors made by the speech decoder, while the second classifier tries to fix deletion errors. By applying the two classifiers together, we achieved a relative reduction in false alarm rate by 25.89% while holding the miscue detection rate constant.

Notes
Associated Lab(s) / Group(s): Project LISTEN
Associated Project(s): Project LISTEN\'s Reading Tutor

Text Reference
Y.-C. Tam, Joseph E. Beck, Jack Mostow, and S. Banerjee, "Training a Confidence Measure for a Reading Tutor that Listens," Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech 2003), October, 2003.

BibTeX Reference
@inproceedings{Beck_2003_4654,
   author = "Y.-C. Tam and Joseph E Beck and Jack Mostow and S. Banerjee",
   title = "Training a Confidence Measure for a Reading Tutor that Listens",
   booktitle = "Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech 2003)",
   month = "October",
   year = "2003",
}