# Towards Automatic Depression Detection:
## 2 Related Work
### Our Motivations and Contributions  
However, current methods have their own weaknesses. To better predict depression severity, the methods proposed in [17; 28; 29; 30] have to design/select a set of effective questions (e.g., questions related to sleep quality, post-traumatic stress disorder, etc.) that can better reveal patients' conditions. These questions are closely related to psychologists' expertise, which is not quite easy to obtain. The method in [32] categorized the queries into seven main topics referring to the Patient Health Questionnaire (PHQ-8) questions. The PHQ-8 questions refer to the 8 questions that constitute the PHQ-8 questionnaire (see supplementary material). The eight questions are designed to investigate how often in the past two weeks the patient suffered from loss of pleasure, low mood, sleep disturbance, lack of energy, eating disorders, low self-esteem, trouble concentrating, and slow movements. The features used for depression detection were extracted from patients' responses sorted by the topics. However, in practice, patients may refuse to respond to the selected questions/topics. If any one of the selected questions/topics is not referred to during the clinical interview, these methods will not be applicable because they cannot construct a complete feature set. As a conclusion, when applied in evaluating new patients' conditions, these methodsmay fail. To avoid relying on the clinicians' expertise and to increase the generalization capability, some methods did not adopt question/topic selection procedures [19; 20; 33]. Instead, they extracted features from responses to the universal questions. However, their performances are not comparable to their counterparts, which are topic related. The above-mentioned drawbacks prompted us to propose a new effective depression detection method with a better generalization ability, a higher performance, and less dependency on expertise. In this work, we propose a new multi-modal depression detection method. Our method utilizes audio features extracted from interview audio, as well text features extracted from patients' responses. It exploits the powerful learning ability of 1D CNN and the bidirectional long short-term memory model with an attention layer to summarize embeddings from audio and text features, respectively. In order to be applicable to various datasets and independent of expertise, the proposed method extracts audio/text features from universal questions instead of manually selected questions/topics. To achieve a better performance, the proposed method integrates two types of embeddings using a multi-modal fusion network and obtains good performances on the two evaluated datasets. Compared with the method described in [32] which also used deep neural networks to fuse different modalities, our framework is different in the adoption of an effective data balancing method, as well as two more powerful deep learning models. More importantly, our proposed method can be applied to various depression related datasets, while the method in [32] was designed only for topic related depression datasets. Our major contributions are summarized as follows:  
(1) We propose a deep learning-based depression detection model accepting speech signals and linguistic content. The proposed method utilizes a 1D CNN model and a BiLSTM model with an attention layer to process audio features and text features, respectively. The two types of embeddings summarized from the 1D CNN and BiLSTM models are concatenated and passed to one-layer fully connected (FC) networks, one of which predicts the presence of depression and the other the depression severity of the patients. The proposed method has no restrictions on the types of questions to be asked in an interview and thus obtains a better generalization capability. These make our method applicable in various situations. (2) A key issue of the problem is how to design and extract representative features. Through extensive comparison experiments, we selected a set of efficient audio features and text features.