Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChakraborty, Debsubhraen_US
dc.contributor.authorXu, Shihaoen_US
dc.contributor.authorYang, Zixuen_US
dc.contributor.authorChua, Victoria Yi Hanen_US
dc.contributor.authorTahir, Yasiren_US
dc.contributor.authorDauwels, Justinen_US
dc.contributor.authorThalmann, Nadia Magnenaten_US
dc.contributor.authorTan, Bhing-Leeten_US
dc.contributor.authorLee, Jimmy Chee Keongen_US
dc.identifier.citationChakraborty, 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.2018.00057en_US
dc.description.abstractSpeech 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.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.description.sponsorshipNMRC (Natl Medical Research Council, S’pore)en_US
dc.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:
dc.subjectSocial sciencesen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titlePrediction of negative symptoms of schizophrenia from objective linguistic, acoustic and non-verbal conversational cuesen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.conference2018 International Conference on Cyberworldsen_US
dc.contributor.researchInstitute for Media Innovation (IMI)en_US
dc.description.versionAccepted versionen_US
dc.subject.keywordsNegative Symptomsen_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Conference Papers
Files in This Item:
File Description SizeFormat 
Cyberworld_2018.pdf212.04 kBAdobe PDFView/Open

Citations 20

Updated on Dec 8, 2022

Web of ScienceTM
Citations 20

Updated on Dec 3, 2022

Page view(s)

Updated on Dec 9, 2022

Download(s) 50

Updated on Dec 9, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.