Lyapunov Spectral Analysis of Speech Embedding Trajectories in Psychosis

• URL: http://arxiv.org/abs/2602.16273v1
• Date: Wed, 18 Feb 2026 08:46:46 GMT
• Title: Lyapunov Spectral Analysis of Speech Embedding Trajectories in Psychosis
• Authors: Jelena Vasic, Branislav Andjelic, Ana Mancic, Dusica Filipovic Djurdjevic, Ljiljana Mihic, Aleksandar Kovacevic, Nadja P. Maric, Aleksandra Maluckov,
• Abstract summary: We analyze speech embeddings from structured clinical interviews of psychotic patients and healthy controls.<n>Lyapunov exponent (LE) spectra are computed from word-level and answer-level embeddings.
• Score: 63.56564189749175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Stable Language Guidance for Vision-Language-Action Models [62.80963701282789]
  Residual Semantic Steering is a probabilistic framework that disentangles physical affordance from semantic execution.<n> RSS achieves state-of-the-art robustness, maintaining performance even under adversarial linguistic perturbations.
  arXiv  Detail & Related papers  (2026-01-07T16:16:10Z)
• ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning [58.01333341218153]
  We propose ClinDEF, a dynamic framework for assessing clinical reasoning in LLMs through simulated diagnostic dialogues.<n>Our method generates patient cases and facilitates multi-turn interactions between an LLM-based doctor and an automated patient agent.<n>Experiments show that ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-12-29T12:58:58Z)
• Mapping Clinical Doubt: Locating Linguistic Uncertainty in LLMs [4.360255198498071]
  This work examines input-side representational sensitivity to linguistic uncertainty in medical text.<n>We propose Model Sensitivity to Uncertainty (MSU), a layerwise probing metric that quantifies activation-level shifts induced by uncertainty cues.
  arXiv  Detail & Related papers  (2025-11-27T12:26:06Z)
• Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection [1.660734109310745]
  We proposed a novel framework that combines Large Language Models (LLMs) with Multiple-Instance Learning (MIL) architecture to enhance interpretability and expression-level reasoning.<n>Our results suggested a psychologically grounded and generalizable approach for fine-grained reasoning in mental health NLP.
  arXiv  Detail & Related papers  (2025-09-22T00:18:58Z)
• Contrast Sensitivity in Multimodal Large Language Models: A Psychophysics-Inspired Evaluation [37.9406446788251]
  We introduce a behavioural method for estimating the Contrast Sensitivity Function (CSF) in Multimodal Large Language Models (MLLMs)<n>Models are queried with structured prompts while viewing noise-based stimuli filtered at specific spatial frequencies.<n>Our results reveal that some models resemble human CSFs in shape or scale, but none capture both.
  arXiv  Detail & Related papers  (2025-08-14T06:07:44Z)
• Hallucination Detection in LLMs with Topological Divergence on Attention Graphs [60.83579255387347]
  Hallucination, i.e., generating factually incorrect content, remains a critical challenge for large language models.<n>We introduce TOHA, a TOpology-based HAllucination detector in the RAG setting.
  arXiv  Detail & Related papers  (2025-04-14T10:06:27Z)
• Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum [4.631774919831163]
  Speech disruptions characteristic of psychosis appear across the spectrum, including in non-clinical individuals.<n>We develop an uncertainty-aware model integrating acoustic and linguistic features to predict symptom severity and psychosis-related traits.
  arXiv  Detail & Related papers  (2025-02-25T15:19:21Z)
• Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis [9.91077163490596]
  We propose a novel, interpretable framework specifically designed to support Parkinson's Disease diagnosis.<n>Through the design of simple yet effective cross-attention mechanisms, the proposed framework offers interpretability from two distinct but complementary perspectives.<n>Our method results competitive with state-of-the-art approaches, while also demonstrating robustness in cross-lingual scenarios.
  arXiv  Detail & Related papers  (2024-12-02T22:23:43Z)
• Generative causal testing to bridge data-driven models and scientific theories in language neuroscience [82.995061475971]
  We present generative causal testing (GCT), a framework for generating concise explanations of language selectivity in the brain.<n>We show that GCT can dissect fine-grained differences between brain areas with similar functional selectivity.
  arXiv  Detail & Related papers  (2024-10-01T15:57:48Z)
• Individualized Dosing Dynamics via Neural Eigen Decomposition [51.62933814971523]
  We introduce the Neural Eigen Differential Equation algorithm (NESDE) NESDE provides individualized modeling, tunable generalization to new treatment policies, and fast, continuous, closed-form prediction. We demonstrate the robustness of NESDE in both synthetic and real medical problems, and use the learned dynamics to publish simulated medical gym environments.
  arXiv  Detail & Related papers  (2023-06-24T17:01:51Z)
• Nonlinear ISA with Auxiliary Variables for Learning Speech Representations [51.9516685516144]
  We introduce a theoretical framework for nonlinear Independent Subspace Analysis (ISA) in the presence of auxiliary variables. We propose an algorithm that learns unsupervised speech representations whose subspaces are independent.
  arXiv  Detail & Related papers  (2020-07-25T14:53:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.