Abstract
Depressive disorder is a common affective disorder, also known as depression, which is characterized by sadness, loss of interest, feelings of guilt or low self-worth and poor concentration. As speech is easy to obtain non-offensively with low-cost, many researchers explore the possibility of depression prediction through speech. Adopting speech signals to recognize depression has important practical significance. Aiming at the problem of the complex structure of the deep neural network method used in the recognition of speech depression and the traditional machine learning methods need to manually extract the features and the low recognition rate. This paper proposes a model that combines residual thinking and attention mechanism. First, depression corpus is designed based on the classic psychological experimental paradigm self-reference effect (SRE), and the speech dataset is labeled; then the attention module is introduced into the residual, and the channel attention is used to learn the features of the channel dimension, the spatial attention feedback the features of the spatial dimension, and the combination of the two to obtain the attention residual unit; finally the stacking unit constructs a speech depression recognition model based on the attention residual network. Experimental results show that compared with traditional machine learning methods, this model obtains better results in the recognition of depression, which can meet the need for actual recognition application of depression. In this study, we not only predict whether person is depressed, but also estimate the severity of depression. In the designed corpus, the depression binary classification of an individual is given based on the severity of depression which is measured using BDI-II scores. Experimental results show that spontaneous speech can obtain better results than automatic speech, and the classification of speech features corresponding to negative questions is better than other tasks under negative emotions. Besides, the recognition accuracy rate of both male and female subjects is higher than that under other emotions.
Highlights
Depressive disorder is a common affective disorder, known as depression, which is characterized by sadness, loss of interest, feelings of guilt or low self-worth, and poor concentration
Literature [12] proposed a depression recognition method based on dynamic convolution neural network (DCNN), which combines manually extracted features and high-level features to achieve the purpose of identifying depression, but manually extracting features require a lot of manpower and material resources
Literature [13] proposed an integrated learning method based on Convolutional Neural Network (CNN), which was evaluated on the AVEC2016 dataset and achieved good results
Summary
Depressive disorder is a common affective disorder, known as depression, which is characterized by sadness, loss of interest, feelings of guilt or low self-worth, and poor concentration. The diagnosis of depression is based on scales, supplemented by the judgment of clinicians The success of this treatment seems to rest a lot with the cooperation of the patient and the experience and professional level of the clinician [3]. With the rapid development of deep learning, researchers have shifted their focus to the construction of deep neural networks to achieve automatic feature extraction and classification. Literature [12] proposed a depression recognition method based on dynamic convolution neural network (DCNN), which combines manually extracted features and high-level features to achieve the purpose of identifying depression, but manually extracting features require a lot of manpower and material resources. Literature [13] proposed an integrated learning method based on Convolutional Neural Network (CNN), which was evaluated on the AVEC2016 dataset and achieved good results.
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