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