文献类型：期刊文献

英文题名：FAD3QN: A Brain-Inspired Deep Reinforcement Learning Model for Speech Depression Detection

作者：Li, Dongdong[1]; Yao, Jia[1]; Wang, Zhe[1]; Yi, Yichao[2]

机构：[1] East China University of Science and Technology, Department of Computer Science and Technology, Shanghai, 200237, China; [2] Changning District Mental Health Center, Information Center, Shanghai, 200335, China

年份：2025

外文期刊名：IEEE Transactions on Computational Social Systems

收录：EI(收录号：20254319390465)

语种：英文

外文关键词：Brain - Deep neural networks - Deep reinforcement learning - Emotion Recognition - Long short-term memory - Psychology computing - Speech communication

摘要：In recent years, the high prevalence and severity of depression have highlighted the urgent need for early detection. Depressed patients exhibit noticeable emotional changes in their speech, but existing detection methods face significant challenges in modeling emotion perception mechanisms. In this article, inspired by the knowledge of reinforcement learning neuroscience and the theory of emotion perception in the limbic system, we propose a brain-inspired model frontal-amygdala double dueling deep Q network for depression detection based on speech. The model simulates the frontal lobe and amygdala-centred brain mechanisms for emotion perception through the reinforcement learning framework of double dueling deep Q-networks, and embeds the skip-connected 1-D convolutional neural network and bidirectional long short-term memory network neural networks to simulate the emotion perception process in the limbic system. In addition, we designed an adaptively tuned reward function to address the data imbalance in depression detection, and incorporated an additional step-size penalty factor to limit the number of incorrect decisions made by the agent during the early stages of training. Experimental results across multiple datasets demonstrate the effectiveness and generalizability of our approach. Meanwhile, the relevant ablation experiments conducted in this article validate the key roles of the frontal and limbic systems in the reward learning and emotion perception process, as well as the importance of the adaptive reward function in solving the data imbalance problem. ? 2014 IEEE.