MFCC-based deep convolutional neural network for audio depression recognition

ABSTRACT

Emotions are a key factor affecting online learning, which has become the new normal due to the COVID-19 pandemic. Identifying students' emotions has important educational implications. Long-term negative emotions can lead to depression, and early identification can help reduce stress and improve learning efficiency. For depressed students in desperate need, current diagnoses are expensive and highly subjective. In response to the above problems, this paper proposes a method for automatic diagnosis of depression with the help of deep convolutional neural networks (Convolutional Neural Network, CNNs). This method inputs the Mel-frequency cepstral coefficients (MFCC) feature maps generated by the preprocessed speech into residual CNNs for training, and adjusts the number of layers and nodes of the network to achieve the highest recognition accuracy. The experimental results are achieved through different residual depths. In ResNet-34, the highest accuracy rate can reach 77%. This will effectively promote the process of emotion recognition research and promote the efficient development of online learning. © 2022 IEEE.