A Novel Smart Depression Recognition Method Using Human-Computer Interaction System

In recent years, depression not only makes patients suffer from psychological pain such as self-blame but also has a high disability mortality rate. Early detection and diagnosis of depression and timely treatment of patients with different levels can improve the cure rate. Because there are quite a few potential depression patients who are not aware of their illness, some even suspect that they are sick but are unwilling to go to the hospital. In response to this situation, this research designed an intelligent depression recognition human-computer interaction system. The main contributions of this research are (1) the use of an audio depression regression model (DR AudioNet) based on a convolutional neural network (CNN) and a long-short-term memory network (LSTM) to identify the prevalence of depression patients. And it uses a multiscale audio differential normalization (MADN) feature extraction algorithm. The MADN feature describes the characteristics of nonpersonalized speech, and two network models are designed based on the MADN features of two adjacent segments of audio. Comparative experiments show that the method is effective in identifying depression. (2) Based on the research conclusion of the previous step, a human-computer interaction system is designed. After the user inputs his own voice, the final recognition result is output through the recognition of the network model used in this research. Visual operation is more convenient for users and has a practical application value