An automatic version of a reading disorder test
Andreas Maier, Flonan Hönig, Stefan Steidl, Elmar Nöth, Stefanie Horndasch, Elisabeth Sauerhöfer, Oliver Kratz, Gunther H. Moll · ACM Transactions on Speech and Language Processing · 2011
We present a novel system to automatically diagnose reading disorders. The system is based on a speech recognition engine with a module for prosodic analysis. The reading disorder test is based on eight different subtests. In each of the subtests, the system achieves a recognition accuracy of at least 95%. As in the perceptual version of the test, the results of the subtests are then joined into a final test result to determine whether the child has a reading disorder. In the final classification stage, the system identifies 98.3% of the 120 children correctly. In the future, our system will facilitate the clinical evaluation of reading disorders.