Objective assessment of pathological speech using distribution regression

Abstract

Objective assessment of pathological speech is an important part of existing systems for automatic diagnosis and treatment of various speech disorders. In this paper, we propose a new regression method for this application. Rather than treating speech samples from each speaker as individual data instances, we treat each speaker's data as a probability distribution. We propose a simple non-parametric learning method to make predictions for out-of-sample speakers based on a probability distance measure to the speakers in the training set. This is in contrast to traditional learning methods that rely on Euclidean distances between individual instances. We evaluate the method on two pathological speech data sets with promising results.