用动力学方法分析精神病患者说话时的语言轨迹，发现其动态不稳定性可区分健康与病态状态。
Lyapunov Spectral Analysis of Speech Embedding Trajectories in Psychosis
- 将语言生成视为高维动态过程，计算词级与回答级嵌入的李雅普诺夫指数谱
- 回答级嵌入中存在正李雅普诺夫指数和高维吸引子，而词级则全部收缩
- 该方法对不同模型嵌入结果稳定，适合研究认知紊乱的非线性特征
我们通过将语言生成视为高维动态过程，分析精神分裂症患者与健康对照组在结构化临床访谈中的语音嵌入。利用两个不同大型语言模型生成的词级和回答级嵌入，计算李雅普诺夫指数（LE）谱，评估结论对不同嵌入表示的鲁棒性。词级嵌入呈现均匀收缩动力学，无正李雅普诺夫指数；而回答级嵌入虽整体收缩，但仍显示多个正李雅普诺夫指数及更高维吸引子。所得李雅普诺夫指数谱能稳健区分精神病与健康言语，尽管精神病组内差异未达统计显著，但最严重病例倾向于占据独特动力学区域。这些结果表明，语音嵌入的非线性动力学不变量为紊乱认知提供了一种物理启发式探测工具，且结论在不同嵌入模型间保持稳定。
原文摘要 · Abstract (English)
We analyze speech embeddings from structured clinical interviews of psychotic patients and healthy controls by treating language production as a high-dimensional dynamical process. Lyapunov exponent (LE) spectra are computed from word-level and answer-level embeddings generated by two distinct large language models, allowing us to assess the stability of the conclusions with respect to different embedding presentations. Word-level embeddings exhibit uniformly contracting dynamics with no positive LE, while answer-level embeddings, in spite of the overall contraction, display a number of positive LEs and higher-dimensional attractors. The resulting LE spectra robustly separate psychotic from healthy speech, while differentiation within the psychotic group is not statistically significant overall, despite a tendency of the most severe cases to occupy distinct dynamical regimes. These findings indicate that nonlinear dynamical invariants of speech embeddings provide a physics-inspired probe of disordered cognition whose conclusions remain stable across embedding models.
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