Assessing Dysarthria severity using global statistics and boosting

A new method for automatic assessment of Dysarthria severity is described. It uses the forward selection method (FSM) on global statistics of low-complexity features to find effective feature sets. FSM is embedded in a boosting algorithm that combines multiple weak classifiers to achieve a single strong classifier. Unlike standard boosting, this uses nonlinear class boundaries and unique feature sets per iteration. Results on a 39 speaker dysarthria database are described.