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Title: Automated lexical analysis of interviews with individuals with schizophrenia
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::Electrical and electronic engineering
Issue Date: 2019
Source: Xu, S., Yang, Z., Chakraborty, D., Tahir, Y., Maszczyk, T., Chua, V. Y. H., . . . Lee, J. C. K. (2019). Automated lexical analysis of interviews with individuals with schizophrenia. Proceedings of the 9th International Workshop on Spoken Dialogue System Technology, 185-197. doi:10.1007/978-981-13-9443-0_16
metadata.dc.contributor.conference: 9th International Workshop on Spoken Dialogue System Technology
Abstract: Schizophrenia is a chronic mental disorder that contributes to poor function and quality of life. We are aiming to design objective assessment tools of schizophrenia. In earlier work, we investigated non-verbal quantitative cues for this purpose. In this paper, we explore linguistic cues, extracted from interviews with patients with schizophrenia and healthy control subjects, conducted by trained psychologists. Specifically, we analyzed the interviews of 47 patients and 24 healthy age-matched control subjects. We applied automated speech recognition and linguistic tools to capture the linguistic categories of emotional and psychological states. Based on those linguistic categories, we applied a binary classifier to distinguish patients from matched control subjects, leading to a classification accuracy of about 86% (by leave-one-out cross-validation); this result seems to suggest that patients with schizophrenia tend to talk about different topics and use different words. We provided an in-depth discussion of the most salient lexical features, which may provide some insights into the linguistic alterations in patients.
ISBN: 9789811394423
DOI: 10.1007/978-981-13-9443-0_16
Schools: School of Electrical and Electronic Engineering 
Lee Kong Chian School of Medicine (LKCMedicine) 
Research Centres: Institute for Media Innovation (IMI) 
Rights: This is a post-peer-review, pre-copyedit version of an article published in Proceedings of the 9th International Workshop on Spoken Dialogue System Technology. The final authenticated version is available online at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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