Towards the Automatic Classification of Reading Disorders in Continuous Text Passages

In this paper, we present an automatic classification approach to identify reading disorders in children. This identification is based on a standardized test. In the original setup the test is performed by a human supervisor who measures the reading duration and notes down all reading errors of the child at the same time. In this manner we recorded tests of 38 children who were suspected to have reading disorders. The data was confronted to an automatic system which employs speech recognition and prosodic analysis to identify the reading errors. In a subsequent classification experiment — based on the speech recognizer’s output, the duration of the test, and prosodic features — 94.7 % of the children could be classified correctly.