Neural Networks in Spontaneous Speech Assessment of Dysphasic Patients

Neural networks can be successfully used for the classification of dysphasic subjects based on their conversational speech using a set of linguistic measures. I shall illustrate the approach with particular reference to its application in classifying agrammatic patients. Linguistic measures can be applied to the transcribed texts of conversational speech of both normal and agrammatic subjects and will quantify the availability of linguistic features which are dependent on word-frequency. The paper presents the results of a cross-validation study using neural networks which take linguistic measurements as inputs for classifying moderate Broca’s aphasics and normal subjects and compares them against those obtained by using a linear discriminant analysis on the same data.