9th Annual Conference of the International Speech Communication Association

Brisbane, Australia
September 22-26, 2008

Combining Neural Network and Rule-Based Systems for Dysarthria Diagnosis

James Carmichael, Vincent Wan, Phil Green

University of Sheffield, UK

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 neuro-muscular 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.

Full Paper

Bibliographic reference.  Carmichael, James / Wan, Vincent / Green, Phil (2008): "Combining neural network and rule-based systems for dysarthria diagnosis", In INTERSPEECH-2008, 2226-2229.