Speech Analysis for Mental Health Assessment Using Support Vector Machines

Abstract

Speech and language dysfunction (SLD) is one of the primary symptoms of mental disorders, such as schizophrenia. Because of the difficulties and subjective nature of SLD assessments, their use in clinical assessment of mental health problems has been limited. Recently, automated discourse analysis methods have been developed and shown the possibility of providing accurate and objective assessments more efficiently. In this chapter, we develop methods of applying Support Vector Machines (SVMs), a computational learning algorithm, in analyzing unstructured conversations of non-native English speakers, both schizophrenias and controls. In this case, the use of conventional language features, such as syntactic and semantic information, is limited because of the nature of participants: multi-cultural, non-native English speakers, and unstructured conversations. A two-level hierarchical classifier was developed that predicts specific SLD items (e.g., poverty of speech) and makes the final diagnostic decisions by combining the SLD assessment results to provide an overall assessment of the underlying mental condition. In particular, we evaluate the SVM classifiers as to their ability to predict SLD items on two mental health assessments: the Thought, Language and Communication Scale (TLC) and the Clinical Language Disorder Rating Scale (CLANG).