用语音特征量化言语协调性，为精神分裂症提供可解释的生物标志物。
Quantifying Articulatory Coordination as a Biomarker for Schizophrenia
- 通过谱图差异与加权指数衰减法分析发音器官协调模式。
- 该方法能有效区分不同症状严重程度患者，且与临床量表高度相关。
- 适合关注精神疾病客观评估、语音分析或可解释AI的临床研究者。
人工智能和深度学习虽提升了医疗诊断能力，但可解释性不足仍限制其临床应用。精神分裂症症状复杂，包括言语混乱与社交退缩，需超越二元诊断的工具来衡量症状严重程度并提供临床洞见。本文提出一种可解释框架，利用发音特征中的谱图差异（eigenspectra difference plots）与加权和指数衰减（WSED）量化声道协调性。谱图差异有效区分了复杂与简单协调模式，而WSED分数能可靠区分两组，歧义仅集中于接近零的狭窄区间。重要的是，WSED分数不仅与总体BPRS评分相关，还反映阳性与阴性症状的平衡：阳性症状显著者表现出更复杂的协调性，而阴性症状强烈者则相反。该方法为精神分裂症提供了透明、敏感的严重程度生物标志物，推动了可解释语音评估工具的发展。
原文摘要 · Abstract (English)
Advances in artificial intelligence (AI) and deep learning have improved diagnostic capabilities in healthcare, yet limited interpretability continues to hinder clinical adoption. Schizophrenia, a complex disorder with diverse symptoms including disorganized speech and social withdrawal, demands tools that capture symptom severity and provide clinically meaningful insights beyond binary diagnosis. Here, we present an interpretable framework that leverages articulatory speech features through eigenspectra difference plots and a weighted sum with exponential decay (WSED) to quantify vocal tract coordination. Eigenspectra plots effectively distinguished complex from simpler coordination patterns, and WSED scores reliably separated these groups, with ambiguity confined to a narrow range near zero. Importantly, WSED scores correlated not only with overall BPRS severity but also with the balance between positive and negative symptoms, reflecting more complex coordination in subjects with pronounced positive symptoms and the opposite trend for stronger negative symptoms. This approach offers a transparent, severity-sensitive biomarker for schizophrenia, advancing the potential for clinically interpretable speech-based assessment tools.
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