Research on Voice Intelligent Monitoring Technology for Early Warning of Recurrence of Depression Disorder

This study aims to collect the voice output of depression patients and healthy subjects, extract the acoustic and semantic parameters, compare the similarities and differences between the depression group and the healthy control group horizontally, and track the depression patients' changes in the rehabilitation stage to construct a voice-based early warning model of depression recurrence. At the same time, the use of EEG technology, nuclear magnetic resonance and near-infrared brain imaging technology to record and analyze the neural activity characteristics behind the voice variation of depression patients, and build a neural mechanism model. And construct the facial recognition function through the convolutional neural network, extract the facial parameters to enrich the intelligent monitoring and early warning technology. 1. Collect linguistic data of depression patients and healthy people collected in the laboratory, as well as data related to changes in the condition of depression patients in daily life and home care after treatment, and construct comparative data and dynamic observations Large database to analyze its voice mutation characteristics; 2. Using EEG technology, nuclear magnetic resonance, and near-infrared brain imaging to record and analyze the neural activity characteristics behind the voice variation of depression patients, and build a neural mechanism model. 3. Use the convolutional neural network to realize the facial recognition function, and extract the facial parameters to enrich the monitoring indicators. 4. Based on the dynamic observation big data of depression speech mutation, construct the speech feature parameter vector of depression recurrence, and use adaptive personalized intelligent learning algorithm to develop intelligent monitoring and early warning technology. 5. Establish monitoring and diagnostic indicators for recurrence early warning, verify the application of the above-mentioned intelligent monitoring and early warning