语音中的抑郁标志物：一种基于声道变量动态特性的方法
Depression Markers in Speech: An Approach based on Tract Variables Dynamics
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中文总结 AI 辅助
该研究基于声道变量动态特性识别新的抑郁生物标志物，利用最大李雅普诺夫指数等量化发音过程特性，在安卓语料库实验中，所提标志物能有效区分抑郁与对照说话者。
中文摘要 AI 辅助
本研究基于声道变量的动态特性识别新的抑郁生物标志物，这些变量代表描述语音发音器官配置的几何特征。该方法的一个关键优势在于能够量化在抑郁背景下以前未被探索的发音过程方面，即可预测性、复杂性和随机性。这些特性分别用最大李雅普诺夫指数、关联维数和样本熵来表征。在安卓语料库上进行了全面实验，该语料库包含64名被临床医生诊断为抑郁的说话者和54名无心理健康问题报告的对照说话者。结果表明，所提出的生物标志物能够有效地区分抑郁和对照说话者，这在朗读和自发语音中都有较高的克利夫斯δ值作为证据。
英文摘要
This study identifies new depression biomarkers based on the dynamical properties of tract variables, which represent geometric features describing the configuration of the speech articulators. A key advantage of this approach lies in its ability to quantify aspects of the articulatory process that have not been previously explored in the context of depression, namely predictability, complexity, and randomness. These properties are respectively characterised using the Largest Lyapunov Exponent, the Correlation Dimension, and the Sample Entropy. Thorough experiments were conducted on the Androids Corpus, a publicly available dataset comprising 64 speakers diagnosed with depression by clinicians and 54 control speakers with no reported history of mental health conditions. The results indicate that the proposed biomarkers effectively discriminate between the depressed and control speakers, as evidenced by the high Cliffs delta values across both read and spontaneous speech.
发表机构
- University of Glasgow（格拉斯哥大学）
- Taif University（泰夫大学）
机构由 AI 辅助整理，请以论文原文为准。
