Prediction of negative symptoms of schizophrenia from objective linguistic, acoustic and non-verbal conversational cues

• Main 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
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering; Social sciences; Engineering::Electrical and electronic engineering; Schizophrenia; Negative Symptoms
• Online Access: https://hdl.handle.net/10356/138204
• Institution: Nanyang Technological University

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.