# Towards Automatic Depression Detection:
## 1 Introduction  
Depression is a global mental disease, whose key features include disruption in emotion experience, communication, and self regulation [1]. More than 264 million people in the world are suffering from depression. In the worst cases, depression can lead to self-harm or even suicide activities. According to the World Health Organization (WHO) reports, about 800,000 people die from severe depression every year [2]. Previous studies have suggested that intervening in the early episode of depression may be crucial to halting the development of depression symptoms [3]. However, early intervention of the disease may be difficult under certain circumstances. Firstly, traditional treatments for depression such as psychotherapy or pharmacological are timely, costly, and sometimes ineffective [4]. For individuals with financial difficulties, the cost of diagnosis and treatment is a heavy burden, which makes patients unwilling to consult a doctor. Secondly, physicians usually assess the severity of depression based on clinical interviews, rating scales, and self-assessments [5]. However, rearing public stigma and other negative consequences brought by the diagnosis, patients sometimes hide their true conditions frompsychologists intentionally [6]. Public stigma includes prejudice, stereotypical beliefs, and discriminatory behaviors towards the depressed person [7], which are the major factors inhibiting individuals with depressive symptoms from seeking help [8; 9]. As a result, psychologists cannot even correctly assess the severity of depression, let alone make effective interventions. In view of these, an efficient automatic depression detection system becomes a necessity, which can help potential patients assess their conditions privately and enhance their willingness to ask for psychologists' help. In addition, an effective automatic depression detection system provides great help to psychologists in the process of diagnosis, especially when the patients are intentionally misleading. Therefore, early intervention can be achieved by providing an objective depression detection system to both mental health professionals and patients [10]. Studies have shown that both nonverbal and verbal behaviors are affected by depression, including facial expressions [11], prosody [1; 12; 13; 14], syntax [15], and semantics [16]. Motivated by these theories and studies, an automatic depression detection system analogizes clinical diagnosis in which verbal representations, facial expressions, and vocal characteristics are analyzed. Currently, approaches to automatic depression detection often utilize information from multiple sources, such as audio, videos, and text extracted from responses [17; 18; 19; 20]. The process of automatic depression detection consists of two steps. In the first step, response audio and/or videos are collected from the participants that are asked a set of questions. Sometimes, text content is also extracted from the audio and videos to improve diagnostic accuracy. In the second step, depression severity is automatically analyzed and assessed using algorithms based on the collected information, which includes voice features, reply content, etc. Although researchers have made some progress in improving diagnostic accuracy, great difficulties still exist in practice. Fist of all, videos of clinical interviews may not be available due to the privacy problem. Secondly, patients may incorrectly report their mental states unconsciously or intentionally, which can mislead the diagnosis. Thirdly, how to extract and fuse representative features from different sources needs more investigation. Therefore, automatic depression detection remains a challenging task for researchers in this field. In this work, we investigate the problem of automatic depression detection and introduce an automatic depression detection method based on the BiLSTM and 1D CNN models to predict the presence of depression, as well as to assess the severity of depressive symptoms.