Why the study?
Due to the complexity of traditional techniques based on clinical diagnosis, there is a need for an automatic depression detection system.
Does a hybrid CNN-SVM model improve the accuracy of audio-based depression detection compared to a CNN model alone?
Does a hybrid CNN-SVM model improve the accuracy of audio-based depression detection compared to a CNN model alone?
A hybrid CNN-SVM model improves the accuracy of automated audio-based depression detection compared to standard CNN models.
Answers are grounded in this paper and cite their sources.
May improve audio-based depression detection accuracy; hypothesis-generating and requires clinical validation before adoption.
Depression is a serious debilitating mental disorder affecting people from all ages all over the world. The number of depression cases increases annually in a continuous way. Due to the complexity of traditional techniques based on clinical diagnosis, there is a need for an automatic detection system of the depression. In this paper we present a novel audio-based approach to automatically detect depression using hybrid model. This model combines convolutional neural networks (CNN) and support vector machines (SVM), where SVM takes the place of the fully connected layers in CNN. In this proposed model, the features are automatically extracted using CNN and the classification is done using the SVM classifier. This approach was evaluated using DAIC-WOZ dataset provided by AVEC 2016 depression analysis sub-challenge. Experimental results showed that our hybrid model achieved an accuracy of 68% which outperform the CNN model (58.57%). Compared to the previous audio-based works using the same DAIC-WOZ dataset, our work showed a significant improvement in terms of accuracy, precision and recall.
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Saidi et al. (2020) studied this question.