用语音分析诊断越南语抑郁症，准确率超86%
Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism
- 设计动态注意力模块增强语音特征提取
- 在越南语数据集上达到87%准确率
- 适合心理健康筛查与远程诊断应用
重度抑郁症是一种普遍且严重的心理健康问题，影响情绪、思维、行为及对世界的感知。由于症状不明显，难以判断是否患病，但语音可作为重要线索：抑郁者常表现不适、悲伤，语速慢、颤抖、情感淡漠。本研究提出动态卷积块注意力模块（Dynamic-CBAM），结合注意力GRU网络，分析人类语音信号以识别情绪状态。实验表明，该模型在VNEMOS数据集上取得0.87的无加权准确率（UA）、0.86的加权准确率（WA）和0.87的F1值，有效识别抑郁或高风险个体，助力早期干预。训练代码已开源。
原文摘要 · Abstract (English)
Major depressive disorder is a prevalent and serious mental health condition that negatively impacts your emotions, thoughts, actions, and overall perception of the world. It is complicated to determine whether a person is depressed due to the symptoms of depression not apparent. However, their voice can be one of the factor from which we can acknowledge signs of depression. People who are depressed express discomfort, sadness and they may speak slowly, trembly, and lose emotion in their voices. In this study, we proposed the Dynamic Convolutional Block Attention Module (Dynamic-CBAM) to utilized with in an Attention-GRU Network to classify the emotions by analyzing the audio signal of humans. Based on the results, we can diagnose which patients are depressed or prone to depression then so that treatment and prevention can be started as soon as possible. The research delves into the intricate computational steps involved in implementing a Attention-GRU deep learning architecture. Through experimentation, the model has achieved an impressive recognition with Unweighted Accuracy (UA) rate of 0.87 and 0.86 Weighted Accuracy (WA) rate and F1 rate of 0.87 in the VNEMOS dataset. Training code is released in https://github.com/fiyud/Emotional-Vietnamese-Speech-Based-Depression-Diagnosis-Using-Dynamic-Attention-Mechanism
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