Audio depression recognition based on deep learning

Depression is characterized by significant and persistent low mood, and is the main type of mood disorder, which seriously affects people's daily work and life. The researchers found that there were many significant differences in speech between normal people and people with depression. This paper presented an audio depression regression prediction model (DR AudioNet) based on convolutional neural networks and long short-term memory. Through research on feature extraction and network architecture, a multiscale audio delta normalization (MADN) feature extraction algorithm was proposed. MADN features described the characteristics of non-personalization speech, and two network models based on DR AudioNet optimization were designed according to the MADN feature of the two adjacent segments. Increase the sample length to obtain more training data without reducing the sample size. The proposed audio depression recognition algorithm, compared with the existing approaches, has improved the prediction accuracy in the diagnosis of depression.