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