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Title: Automatic verbal analysis of interviews with schizophrenic patients
Authors: Xu, Shihao
Yang, Zixu
Chakraborty, Debsubhra
Tahir, Yasir
Maszczyk, Tomasz
Chua, Victoria Yi Han
Dauwels, Justin
Thalmann, Daniel
Thalmann, Nadia Magnenat
Tan, Bhing-Leet
Lee, Jimmy Chee Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Xu, S., Yang, Z., Chakraborty, D., Tahir, Y., Maszczyk, T., Chua, V. Y. H., . . . Lee, J. C. K. (2019). Automatic verbal analysis of interviews with schizophrenic patients. Proceedings of the 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). doi:10.1109/ICDSP.2018.8631830
metadata.dc.contributor.conference: 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)
Abstract: Schizophrenia is a long-term mental disease associated with language impairments that affect about one percent of the population. Traditional assessment of schizophrenic patients is conducted by trained professionals, which requires tremendous resources of time and effort. This study is part of a larger research objective committed to creating automated platforms to aid clinical diagnosis and understanding of schizophrenia. We have analyzed non-verbal cues and movement signals in our previous work. In this study, we explore the feasibility of using automatic transcriptions of interviews to classify patients and predict the observability of negative symptoms in schizophrenic patients. Interview recordings of 50 schizophrenia patients and 25 age-matched healthy controls were automatically transcribed by a speech recognition toolkit. After which, Natural Language Processing techniques were applied to automatically extract the lexical features and document vectors of transcriptions. Using these features, we applied ensemble machine learning algorithm (by leave-one-out cross-validation) to predict the Negative Symptom Assessment subject ratings of schizophrenic patients, and to classify patients from controls, achieving a maximum accuracy of 78.7%. These results indicate that schizophrenic patients exhibit significant differences in lexical usage compared with healthy controls, and the possibility of using these lexical features in the understanding and diagnosis of schizophrenia.
ISSN: 978-981-13-9443-0
DOI: 10.1109/ICDSP.2018.8631830
Schools: School of Electrical and Electronic Engineering 
Lee Kong Chian School of Medicine (LKCMedicine) 
Research Centres: Institute for Media Innovation (IMI) 
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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