Quantitative classification of conversational language using artificial neural networks

Abstract

In this paper I shall describe the use of artificial neural networks for the classification of subjects based on their conversational speech using a set of linguistic measures, with particular reference to the application of this approach in classifying dysphasic patients. These linguistic measures can be applied to the transcribed texts of conversational speech of both normal and dysphasic subjects, and will quantify the availability of linguistic features which are dependent on word frequency. The paper presents the results of a crossvalidation study using neural networks and compares them against those obtained using a linear discriminant analysis on the same data.