How can an automated tutor detect children's off-task utterances? To answer this question, we trained SVM classifiers on a corpus of 495 children's 36,492 computer-assisted oral reading utterances. On a test set of 620 utterances by 10 held-out readers, the classifier correctly detected 88% of off-task utterances and misclassified 17% of on-task utterances as off-task. As a test of generality, we applied the same classifier to 20 children's 410 responses to vocabulary questions. The classifier detected 84% of off-task utterances but misclassified 57% of on-task utterances. Acoustic and lexical features helped detect off-task speech in both tasks.
Bibliographic reference. Chen, Wei / Mostow, Jack (2011): "A tale of two tasks: detecting children's off-task speech in a reading tutor", In INTERSPEECH-2011, 1621-1624.