Detecting depression from facial actions and vocal prosody

By Jeffrey F. Cohn, T. S. Kruez, Iain Matthews, Ying Yang, Minh Hoai Nguyen, Michael T. Padilla, Feng Zhou, Fernando De la Torre. 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009

505 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/e423265bda7cc1260fe37813facb9b904429aa81

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Citations in the corpus

Max-Margin Early Event Detectors (2012, background): “This section discusses previous work on early detection and event detection.”
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019, background): “There is growing interest in using automatic human behaviour analysis for computer-aided depression diagnosis based on behavioural cues such as facial expressions and speech prosody, because of convincing evidences that depression and related mental health disorders are associated with changes in pa”
A review of depression and suicide risk assessment using speech analysis (2015, methodology): “work has been carried out into the automatic analysis of depression using non speech based behavioural signals such as facial activity (Cohn et al., 2009; Joshi et al., 2013; Scherer et al., 2013c) hand movements and leg fidgeting (Scherer et al.”
DepAudioNet: An Efficient Deep Model for Audio based Depression Classification (2016, methodology): “ADD is a young topic and has not been investigated until 2009 [8].”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, background): “studied the difference between directly predicting depression from video by using a global geometrical representation (AAM), indirectly predicting depression from video by analyzing previously detected facial AUs and prediction depression from audio cues [187].”
AVEC 2013: the continuous audio/visual emotion and depression recognition challenge (2013, background): “Categories and Subject Descriptors J [Computer Applications]: Miscellaneous; D.2.8 [Software Engineering]: Metrics—complexity measures, performance measures Keywords Affective Computing, Emotion Recognition, Speech, Facial Expression, Challenge”
Advanced Structured Prediction (2014, background): “Convex relaxations of combinatorial problems, as appearing in computer vision, processing of medical data or analysis of transport networks, often contain millions of variables and hundreds of thousands of constraints.”
Facial Expression Analysis (2011, background): “Facial expression figures prominently in research on almost every aspect of emotion, including psychophysiology [66], neural correlates [39], development [84], perception [2], addiction [47], social processes [52], depression [27] and other emotion disorders [116].”
SimSensei kiosk: a virtual human interviewer for healthcare decision support (2014, background): “Examples of observed differences in verbal behavior in depressed individuals include differences in speaker-switch durations and variability in vocal fundamental frequency [6], decreased speech, slow speech, delays in delivery, and long silent pauses [12], as well as differences in certain lexical f”