Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals
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 acousti...
| Main Authors: | , , , , , , , , , , |
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| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10356/140528 |
| _version_ | 1826125732768645120 |
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| author | 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 |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering 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 |
| author_sort | Chakraborty, Debsubhra |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:41:10Z |
| format | Conference Paper |
| id | ntu-10356/140528 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:41:10Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1405282020-05-29T14:07:59Z Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals 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 School of Electrical and Electronic Engineering 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Institute for Media Innovation (IMI) Engineering Engineering::Electrical and electronic engineering Schizophrenia Affective Prosody 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. NRF (Natl Research Foundation, S’pore) NMRC (Natl Medical Research Council, S’pore) Accepted version 2020-05-29T14:07:59Z 2020-05-29T14:07:59Z 2018 Conference Paper 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 https://hdl.handle.net/10356/140528 10.1109/ICASSP.2018.8462102 6024 6028 en © 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 application/pdf |
| spellingShingle | Engineering Engineering::Electrical and electronic engineering Schizophrenia Affective Prosody 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 Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals |
| title | Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals |
| title_full | Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals |
| title_fullStr | Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals |
| title_full_unstemmed | Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals |
| title_short | Prediction of negative symptoms of schizophrenia from emotion related low-level speech signals |
| title_sort | prediction of negative symptoms of schizophrenia from emotion related low level speech signals |
| topic | Engineering Engineering::Electrical and electronic engineering Schizophrenia Affective Prosody |
| url | https://hdl.handle.net/10356/140528 |
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