Facial geometry and speech analysis for depression detection
Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depr...
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Institute of Electrical and Electronics Engineers Inc.
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029121274&doi=10.1109%2fEMBC.2017.8037103&partnerID=40&md5=a8c5cb70311a24f926e4bfcda31410d1 http://eprints.utp.edu.my/20022/ |
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my.utp.eprints.200222018-04-22T14:37:57Z Facial geometry and speech analysis for depression detection Pampouchidou, A. Simantiraki, O. Vazakopoulou, C.-M. Chatzaki, C. Pediaditis, M. Maridaki, A. Marias, K. Simos, P. Yang, F. Meriaudeau, F. Tsiknakis, M. Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8 for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation. © 2017 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029121274&doi=10.1109%2fEMBC.2017.8037103&partnerID=40&md5=a8c5cb70311a24f926e4bfcda31410d1 Pampouchidou, A. and Simantiraki, O. and Vazakopoulou, C.-M. and Chatzaki, C. and Pediaditis, M. and Maridaki, A. and Marias, K. and Simos, P. and Yang, F. and Meriaudeau, F. and Tsiknakis, M. (2017) Facial geometry and speech analysis for depression detection. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS . pp. 1433-1436. http://eprints.utp.edu.my/20022/ |
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Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8 for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation. © 2017 IEEE. |
| format |
Article |
| author |
Pampouchidou, A. Simantiraki, O. Vazakopoulou, C.-M. Chatzaki, C. Pediaditis, M. Maridaki, A. Marias, K. Simos, P. Yang, F. Meriaudeau, F. Tsiknakis, M. |
| spellingShingle |
Pampouchidou, A. Simantiraki, O. Vazakopoulou, C.-M. Chatzaki, C. Pediaditis, M. Maridaki, A. Marias, K. Simos, P. Yang, F. Meriaudeau, F. Tsiknakis, M. Facial geometry and speech analysis for depression detection |
| author_facet |
Pampouchidou, A. Simantiraki, O. Vazakopoulou, C.-M. Chatzaki, C. Pediaditis, M. Maridaki, A. Marias, K. Simos, P. Yang, F. Meriaudeau, F. Tsiknakis, M. |
| author_sort |
Pampouchidou, A. |
| title |
Facial geometry and speech analysis for depression detection |
| title_short |
Facial geometry and speech analysis for depression detection |
| title_full |
Facial geometry and speech analysis for depression detection |
| title_fullStr |
Facial geometry and speech analysis for depression detection |
| title_full_unstemmed |
Facial geometry and speech analysis for depression detection |
| title_sort |
facial geometry and speech analysis for depression detection |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2017 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029121274&doi=10.1109%2fEMBC.2017.8037103&partnerID=40&md5=a8c5cb70311a24f926e4bfcda31410d1 http://eprints.utp.edu.my/20022/ |
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13.211869 |