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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Main Authors: Pampouchidou, A., Simantiraki, O., Vazakopoulou, C.-M., Chatzaki, C., Pediaditis, M., Maridaki, A., Marias, K., Simos, P., Yang, F., Meriaudeau, F., Tsiknakis, M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20022 /
Published: Institute of Electrical and Electronics Engineers Inc. 2017
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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spelling 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/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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_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/
_version_ 1741196303356919808
score 11.62408

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