# Towards Automatic Depression Detection:
## 2 Related Work  
Automatic depression detection did not attract much attention until 2009 [21]. [PERSON] et al. [21] extracted manual Facial Action Coding System (FACS) features, Active Appearance Modeling (AAM), and pitches to measure facial and vocal expression. They used simple classifiers (i.e., SVM and logistic regression) to assess depression severity and reached a high accuracy. Since then, automatic depression detection based on machine learning techniques has aroused increasing interest from researchers [22; 23; 24; 11; 25; 26].  
In general, methods of automatic depression detection first extract different types of features from interview audio/videos of patients who are asked a set of carefully crafted questions from different topics. Then, models are trained using the extracted features to predict the presence of depression or to assess the severity of depression for the patients.  
Early studies of automatic depression detection devoted great efforts to extracting effective features from closely correlated questions designed for interviews. In [27], [PERSON] et al. demonstrated that some key questions (e.g., \"Do you need help?\") can improve diagnostic accuracy. [PERSON] et al. [28] conducted content analysis of transcripts to manually select depression related questions. They constructed a decision tree based on the selected questions to predict the patients' depressive conditions. Similarly, [PERSON] et al. [29] analyzed the interview transcripts of participants and extracted text features from the questions related to certain topics (e.g., sleep quality, introversion, parents, etc.). They used random forest to detect depression tendency. [PERSON] and [PERSON] [30] performed context-aware analysis with topic modeling to preserve important temporal details in long interviews. Utilizing the Gaussian staircase regressor, [PERSON] et al. [17] achieved a good performance by analyzing the semantic context to obtain coarse depressive descriptors.  
Inspired by the emerging deep learning techniques, [PERSON] et al. [31] proposed a single hybrid deep model, which was trained jointly with the acoustic and linguistic features. They found that using deep networks to fuse features could achieve better results for deception detection. Deep learning approaches to fuse multi-modal features in depression detection appear to be particularly promising. [PERSON] et al. [18] proposed a depression detection method based on the Deep Convolution Neural Network (DCNN) and the Deep Neural Network (DNN). They also designed a set of new video and audio descriptors to train the model. [PERSON] et al. [19] used the Long Short-Term Memory (LSTM) network to assess depression tendency. They used text features and audio features that were highly correlated with depression severity. [PERSON] et al. [32] proposed a data augmentation procedure based on topic modeling. They first labeled each participant's transcript and audio with corresponding topics. Then, for each participant, a subset of the labeled data was extracted and combined to make a new training sample. They used a deep learning model, namely Transformer, for text feature modeling and a deep One-Dimensional Convolutional Neural Network (1D CNN) for audio feature modeling. The combination of the two models achieved state-of-the-art performance. Based on LSTM and CNN, [PERSON] et al. [33] encoded the depression related temporal clues in the vocal modality to predict the presence of depression. To address the data imbalance issue, they performed random over-sampling on audio recordings. [PERSON] [20] utilized a causal CNN to predict depressive symptom severity by summarizing embeddings, which captured long-term acoustic, visual, and linguistic elements. In [34], [PERSON] et al. put forward a text-based multi-task BiLSTM model with pre-trained word embeddings and acquired a high performance on the Distress Analysis Interview Corpus Wizard-of-Oz (DAIC-WoZ) dataset.