Facial Geometry and Speech Analysis for Depression Detection
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Facial Geometry and Speech Analysis for Depression Detection |
| Type de publication | Conference Paper |
| Year of Publication | 2017 |
| Auteurs | Pampouchidou A., Simantiraki O., Vazakopoulou C.-M, Chatzaki C., Pediaditis M., Maridaki A., Marias K., Simos P., Yang F., Meriaudeau F., Tsiknakis M. |
| Conference Name | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
| Publisher | IEEE Engn Med & Biol Soc; PubMed; MEDLINE; Korean Soc Med & Biol Engn |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5090-2809-2 |
| Résumé | 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. |