Automatic analysis of speech quality for aphasia treatment

Abstract

Aphasia is a common language disorder which can severely affect an individual's ability to communicate with others. Aphasia rehabilitation requires intensive practice accompanied by appropriate feedback, the latter of which is difficult to satisfy outside of therapy. In this paper we take a first step towards developing an intelligent system capable of providing feedback to patients with aphasia through the automation of two typical therapeutic exercises, sentence building and picture description. We describe the natural speech corpus collected from our interaction with clients in the University of Michigan Aphasia Program (UMAP). We develop classifiers to automatically estimate speech quality based on human perceptual judgment. Our automatic prediction yields accuracies comparable to the average human evaluator. Our feature selection process gives insights into the factors that influence human evaluation. The results presented in this work provide support for the feasibility of this type of system.