Using a student model to improve a computer tutor’s speech recognition

Abstract

Intelligent computer tutors can derive much of their power from having a student model that describes the learner’s competencies. However, constructing a student model is challenging for computer tutors that use automated speech recognition (ASR) as input. This paper reports using ASR output from a computer tutor for reading to compare two models of how students learn to read words: a model that assumes students learn words as whole-unit chunks, and a model that assumes students learn the individual letter→ sound mappings that make up words. We use the data collected by the ASR to show that a model of letter→ sound mappings better describes student performance. We then compare using the student model and the ASR, both alone and in combination, to predict which words the student will read correctly, as scored by a human transcriber. Surprisingly, majority class has a higher classification accuracy than the ASR. However, we demonstrate that the ASR output still has useful information, and that classification accuracy is not a good metric for this task, and the Area Under Curve (AUC) of ROC curves is a superior scoring method. The AUC of the student model is statistically reliably better (0.670 vs. 0.550) than that of the ASR, which in turn is reliably better than majority class. These results show that ASR can be used to compare theories of how students learn to read words, and modeling individual learner’s proficiencies may enable improved speech recognition.