Abstract
Depression has become one of the main afflictions that threaten people's mental health. However, the current traditional diagnosis methods have certain limitations, so it is necessary to find a method of objective evaluation of depression based on intelligent technology to assist in the early diagnosis and treatment of patients. Because the abnormal speech features of patients with depression are related to their mental state to some extent, it is valuable to use speech acoustic features as objective indicators for the diagnosis of depression. In order to solve the problem of the complexity of speech in depression and the limited performance of traditional feature extraction methods for speech signals, this article suggests a Three-Dimensional Convolutional filter bank with Highway Networks and Bidirectional GRU (Gated Recurrent Unit) with an Attention mechanism (in short 3D-CBHGA), which includes two key strategies. (1) The three-dimensional feature extraction of the speech signal can timely realize the expression ability of those depression signals. (2) Based on the attention mechanism in the GRU network, the frame-level vector is weighted to get the hidden emotion vector by self-learning. Experiments show that the proposed 3D-CBHGA can well establish mapping from speech signals to depression-related features and improve the accuracy of depression detection in speech signals.
Highlights
Mental pressure or depression from work and life has become one of the main threats to our health (Kermc et al, 2019)
The input of the network are MFCC (Mel-Frequency Cepstral Coefficients) (Zaidan and Salam, 2016) features extracted from speech signals, and 39-dimensional MFCC features are extracted from each frame
The 3D-CBHGA model proposed in this paper was applied to depression detection with other classical models, and the results of performance comparison were analyzed
Summary
Mental pressure or depression from work and life has become one of the main threats to our health (Kermc et al, 2019). According to relevant statistical results (World Health Organization, 2020), it is estimated that there are more than 350 million patients with depression worldwide, and there are more than 95 million patients with depression in China, basically denoting about 30% of the global average level. Depression has been plagued by a low recognition rate, low consultation rate, and low treatment rate, and it is highly likely to be seriously underestimated (Huang et al, 2019). The diagnosis of depression is mainly based on questionnaire surveys, supplemented by doctors’ judgment. Its accuracy depends heavily on patient cooperation and physician expertise, and early diagnosis and reassessment of depression are limited.
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