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Title: Prediction of negative symptoms of schizophrenia from objective linguistic, acoustic and non-verbal conversational cues
Authors: Chakraborty, Debsubhra
Xu, Shihao
Yang, Zixu
Chua, Victoria Yi Han
Tahir, Yasir
Dauwels, Justin
Thalmann, Nadia Magnenat
Tan, Bhing-Leet
Lee, Jimmy Chee Keong
Keywords: Engineering
Social sciences
Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Chakraborty, D., Xu, S., Yang, Z., Chua, V. Y. H., Tahir, Y., Dauwels, J., . . ., Lee, J. C. K. (2018). Prediction of negative symptoms of schizophrenia from objective linguistic, acoustic and non-verbal conversational cues. 2018 International Conference on Cyberworlds (CW), 280-283. doi:10.1109/CW.2018.00057
Abstract: Speech disorders are among the salient characteristics of negative symptoms of schizophrenia. Such impairments are often exhibited through disorganized speech, inappropriate affective prosody, and poverty of speech. The current method of detecting such symptoms requires the expertise of a trained clinician, which may be prohibitive due to cost, stigma or high patient-to-clinician ratio. An objective method to extract nonverbal and verbal speech-related cues can help to automate and simplify the assessment method of severity of speechrelated symptoms of schizophrenia. In this paper, a novel automated method is presented which uses speech content from schizophrenic patients to predict the clinician-assigned subjective ratings of their negative symptoms. Specifically, the interviews of 50 schizophrenia patients were recorded and features related to acoustics, linguistics and non-verbal conversation were extracted. The subjective ratings can be accurately predicted from the objective features with an accuracy of 64-82% using machine learning algorithms with leave-one-out cross-validation. Our findings support the utility of automated speech analysis to aid clinician diagnosis, monitoring and understanding of schizophrenia.
ISBN: 9781538673157
DOI: 10.1109/CW.2018.00057
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
Appears in Collections:EEE Conference Papers

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