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With the acceleration of the pace of work and life, people have to face moreand more pressure, which increases the possibility of suffering fromdepression. However, many patients may fail to get a timely diagnosis due tothe serious imbalance in the doctor-patient ratio in the world. Promisingly,physiological and psychological studies have indicated some differences inspeech and facial expression between patients with depression and healthyindividuals. Consequently, to improve current medical care, many scholars haveused deep learning to extract a representation of depression cues in audio andvideo for automatic depression detection. To sort out and summarize theseworks, this review introduces the databases and describes objective markers forautomatic depression estimation (ADE). Furthermore, we review the deep learningmethods for automatic depression detection to extract the representation ofdepression from audio and video. Finally, this paper discusses challenges andpromising directions related to automatic diagnosing of depression using deeplearning technologies.

Deep Learning for Depression Recognition with Audiovisual Cues: A Review