Speech analysis for mental health assessment using support vector machines

Song, Insu and Diederich, Joachim (2014). Speech analysis for mental health assessment using support vector machines. In Margaret Lech, Peter Yellowlees, Insu Song and Joachim Diederich (Ed.), Mental health informatics (pp. 79-105) Heidelberg, Germany: Springer. doi:10.1007/978-3-642-38550-6_5


Author Song, Insu
Diederich, Joachim
Title of chapter Speech analysis for mental health assessment using support vector machines
Title of book Mental health informatics
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2014
Sub-type Article (original research)
DOI 10.1007/978-3-642-38550-6_5
Year available 2014
Series Studies in Computational Intelligence
ISBN 9783642385490
9783642385506
ISSN 1860-949X
1860-9503
Editor Margaret Lech
Peter Yellowlees
Insu Song
Joachim Diederich
Volume number 491
Chapter number 5
Start page 79
End page 105
Total pages 27
Total chapters 13
Collection year 2015
Language eng
Abstract/Summary 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).
Q-Index Code B1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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