Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140528
Title: Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals
Authors: Chakraborty, Debsubhra
Yang, Zixu
Tahir, Yasir
Maszczyk, Tomasz
Dauwels, Justin
Thalmann, Nadia
Zheng, Jianmin
Maniam, Yogeswary
Nur Amirah
Tan, Bhing-Leet
Lee, Jimmy Chee Keong
Keywords: Engineering
Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Chakraborty, D., Yang, Z., Tahir, Y., Maszczyk, T., Dauwels, J., Thalmann, N., . . ., Lee, J. C. K. (2018). Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6024-6028. doi:10.1109/ICASSP.2018.8462102
Abstract: Negative symptoms of schizophrenia are often associated with the blunting of emotional affect which creates a serious impediment in the daily functioning of the patients. Affective prosody is almost always adversely impacted in such cases, and is known to exhibit itself through the low-level acoustic signals of prosody. To automate and simplify the process of assessment of severity of emotion related symptoms of schizophrenia, we utilized these low-level acoustic signals to predict the expert subjective ratings assigned by a trained psychologist during an interview with the patient. Specifically, we extract acoustic features related to emotion using the openSMILE toolkit from the audio recordings of the interviews. We analysed the interviews of 78 paid participants (52 patients and 26 healthy controls) in this study. The subjective ratings could be accurately predicted from the objective openSMILE acoustic signals with an accuracy of 61-85% using machine-learning algorithms with leave-one-out cross-validation technique. Furthermore, these objective measures can be reliably utilized to distinguish between the patient and healthy groups, as supervised learning methods can classify the two groups with 79-86% accuracy.
URI: https://hdl.handle.net/10356/140528
DOI: 10.1109/ICASSP.2018.8462102
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: https://doi.org/10.1109/ICASSP.2018.8462102
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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