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.

翻译：我们通过将语言生成视为高维动力学过程，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言语嵌入。基于两种不同大语言模型生成的词级和回答级嵌入计算李雅普诺夫指数谱，使我们能够评估结论在不同嵌入表示下的稳定性。词级嵌入呈现均匀收缩动力学特征，未出现正李雅普诺夫指数；而回答级嵌入虽整体呈收缩态势，却展现出若干正指数及更高维度的吸引子。所得李雅普诺夫指数谱能稳健区分精神分裂症与健康言语，尽管最严重病例呈现占据不同动力学区域趋势，但精神分裂症组内的整体差异未达统计显著性。这些发现表明，言语嵌入的非线性动力学不变量为认知障碍提供了物理学启发的探测手段，其结论在不同嵌入模型间保持稳定。