ISCA International Workshop on Speech and Language Technology in Education (SLaTE 2011)

Venice, Italy
August 24-26, 2011

Combining Acoustic and Language Model Miscue Detection Methods for Adult Dyslexic Read Speech

Morten Højfeldt Rasmussen, Børge Lindberg, Zheng-Hua Tan

1Department of Electronic Systems, Aalborg University, Denmark

One important feature of automatic reading tutors is their ability to detect miscues in order to provide feedback to the student and/or to automatically evaluate the student’s reading proficiency. The focus of this paper is on improving accuracy of detecting miscues in dyslexic read speech. We present a miscue detection method that combines a specialized language model and the goodness of pronunciation (GOP) score. The language model is augmented with a subset of the real word substitutions that are observed in the training set. Experiments have been conducted on a corpus containing adult dyslexic read speech. At a miscue detection rate of 34% the false rejection rate (FRR) using only the specialized language model is 2.6%, for the GOP score it’s 3.4%, whereas for a combination of the two miscue detection methods the FRR is only 1.8%, which is a 31% relative improvement of FRR when compared to the specialized language model method.
Index Terms. miscue detection, automatic reading tutor, dyslexia, read speech

Full Paper

Bibliographic reference.  Rasmussen, Morten Højfeldt / Lindberg, Børge / Tan, Zheng-Hua (2011): "Combining acoustic and language model miscue detection methods for adult dyslexic read speech", In SLaTE-2011, 21-24.