用图神经网络整合语音片段关联，提升帕金森病检测准确率
Graph Neural Networks for Parkinsons Disease Detection
- 将语音片段建模为节点，通过相似性构建边，形成语义关系图
- 在多个数据集上达到90%以上准确率，显著优于传统孤立分析方法
- 特别适合处理标注噪声多、症状不一致的临床语音数据
尽管当前帕金森病（PD）检测方法表现良好，但通常孤立分析单个语音片段，难以捕捉跨片段的言语障碍特征关联。帕金森患者语音中的构音障碍线索在不同片段间具有相关性，而孤立分析会忽略这种关系。此外，并非所有语音片段都表现出明显症状，导致标签噪声影响模型性能与泛化能力。为此，本文提出一种基于图卷积网络（GCN）的新框架：将语音片段作为节点，通过边表示片段间的相似性，使模型能聚合全图的构音线索，有效利用片段间关系并降低噪声影响。实验表明，该方法在多个公开数据集上实现超过90%的检测准确率，验证了其有效性，并揭示了其内在机制。
原文摘要 · Abstract (English)
Despite the promising performance of state of the art approaches for Parkinsons Disease (PD) detection, these approaches often analyze individual speech segments in isolation, which can lead to suboptimal results. Dysarthric cues that characterize speech impairments from PD patients are expected to be related across segments from different speakers. Isolated segment analysis fails to exploit these inter segment relationships. Additionally, not all speech segments from PD patients exhibit clear dysarthric symptoms, introducing label noise that can negatively affect the performance and generalizability of current approaches. To address these challenges, we propose a novel PD detection framework utilizing Graph Convolutional Networks (GCNs). By representing speech segments as nodes and capturing the similarity between segments through edges, our GCN model facilitates the aggregation of dysarthric cues across the graph, effectively exploiting segment relationships and mitigating the impact of label noise. Experimental results demonstrate theadvantages of the proposed GCN model for PD detection and provide insights into its underlying mechanisms
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