Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105913
Title: Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia
Authors: Tahir, Yasir
Yang, Zixu
Chakraborty, Debsubhra
Thalmann, Nadia
Thalmann, Daniel
Maniam, Yogeswary
Tan, Bhing-Leet
Dauwels, Justin
Nur Amirah Abdul Rashid
Lee, Jimmy Chee Keong
Keywords: Verbal Communication
DRNTU::Science::Medicine
Speech
Issue Date: 2019
Source: Tahir, Y., Yang, Z., Chakraborty, D., Thalmann, N., Thalmann, D., Maniam, Y., . . . Dauwels, J. (2019). Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia. PLOS ONE, 14(4), e0214314-. doi:10.1371/journal.pone.0214314
Series/Report no.: PLOS ONE
Abstract: Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.
URI: https://hdl.handle.net/10356/105913
http://hdl.handle.net/10220/48840
DOI: 10.1371/journal.pone.0214314
Schools: School of Electrical and Electronic Engineering 
Lee Kong Chian School of Medicine (LKCMedicine) 
Organisations: Institute for Media Innovation
Rights: © 2019 Tahir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
LKCMedicine Journal Articles

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