Identification of depression state based on multi-scale acoustic features in interrogation environment

Depression diagnosis based on speech signals has the advantages of non-invasiveness, low cost, and few restrictions on portability. The research on the recognition of the depression state is carried out based on the acoustic information in the speech signal. Aiming at the interview dialogue speech in the consultation environment, a hierarchical attention temporal convolutional network (HATCN) acoustic depression recognition model is proposed. For sentence acoustic feature learning, a regional attention mechanism is introduced to extract multi-scale sentence features; for segment acoustic feature extraction, the traditional attention mechanism is used to calculate, which is in line with human cognitive mechanism. In addition, a periodic focal loss function is introduced to address the imbalance of positive and negative samples in depression diagnosis. Experiments show that the proposed acoustic depression recognition model has a certain improvement in recognition performance compared with other methods. At the same time, the influence of noise on the recognition of acoustic depression in the real consultation environment is analysed through experiments, and the data enhancement is carried out utilising speech noise, which proves the effectiveness of the data expansion of speech noise.

Automated summary

Depression diagnosis based on speech signals offers advantages such as non-invasiveness, low cost, and portability. This research proposes a hierarchical attention temporal convolutional network (HATCN) acoustic depression recognition model that utilizes acoustic information in speech signals. Experimental results show that the proposed model improves recognition performance compared to other methods. Additionally, the impact of noise on acoustic depression recognition in real consultation environments is analyzed, and data enhancement using speech noise proves to be effective.