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

本研究解决了现有帕金森病检测方法分析单个语音段时无法利用不同发言者之间的语段关系的不足。通过提出一种新颖的图卷积网络（GCN）框架，该框架将语音段表示为节点，利用边来捕捉语段之间的相似性，有效聚合和利用了语音失调线索。实验结果表明，所提出的GCN模型在帕金森病检测中具有显著优势，并揭示了其潜在的机制。

基于图神经网络的帕金森病检测