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Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text., , , , , , , , , and 2 other author(s). AVEC@ACM Multimedia, page 27-34. ACM, (2016)Machine Learning Techniques for Automatic Depression Assessment., , , and . TSP, page 1-5. IEEE, (2018)Quantitative comparison of motion history image variants for video-based depression assessment., , , , , , , , , and 1 other author(s). EURASIP J. Image and Video Processing, (2017)Stress Detection from Speech Using Spectral Slope Measurements., , , and . MindCare/Fabulous, volume 207 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 41-50. Springer, (2016)Designing a framework for assisting depression severity assessment from facial image analysis., , , , , and . ICSIPA, page 578-583. IEEE, (2015)Glottal Source Features for Automatic Speech-Based Depression Assessment., , , , and . INTERSPEECH, page 2700-2704. ISCA, (2017)Facial geometry and speech analysis for depression detection., , , , , , , , , and 1 other author(s). EMBC, page 1433-1436. IEEE, (2017)Automatic detection of visual cues associated to depression. (Détection automatique des repères visuels associés à la dépression).. University of Burgundy - Franche-Comté, France, (2018)Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes., , , , , , and . EMBC, page 3835-3838. IEEE, (2016)Preliminary Evaluation of a Web-Oriented Assessment Tool for Emotion Recognition., , , , , , , and . pHealth, volume 224 of Studies in Health Technology and Informatics, page 95-100. IOS Press, (2016)