Combining neural network and rule-based systems for dysarthria diagnosis.

This study reports on the development of a diagnostic expert system – incorporating a multilayer perceptron (MLP) – designed to identify any sub-type of dysarthria (loss of neuromuscular control over the articulators) manifested by a patient undergoing a Frenchay Dysarthria Assessment (FDA) evaluation. If sufficient information is provided describing pathological features of the patient’s speech, the rule-based classifier (RBC) can out-perform the MLP in terms of rendering a more accurate and consistent diagnosis. The combination MLP/RBC developed during this study realised an overall improvement in diagnostic accuracy of 9.3% (absolute) for a selection of dysarthric cases, representing a substantial improvement over the benchmark system which – unlike the MLP/RBC – cannot directly process acoustic data.