Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols
2026-08-31 • Computation and Language
Computation and Language
AI summaryⓘ
The authors studied how to improve mental health screening from speech and text by recognizing that different types of speech tests provide different kinds of evidence. They found that treating all speech protocols the same can lead to inaccurate or false symptom detection. To fix this, they created a system called EviBound that carefully checks which parts of the evidence should be used and ignores unsupported information. Their method performed better than previous models and avoided making unsupported claims. This work helps make AI-based mental health tools safer and more reliable by respecting the differences in speech data types.
multimodal mental health screeningspeech protocolsevidence-bounded reasoninglarge language models (LLMs)Depression AUROCacoustic consensusprotocol-aware systemsclinical natural language processing (NLP)model hallucinationevidence control framework
Authors
Chengyuan Gao, Jiang Wu, Tao Lu, Jiayan Guo, Mingkun Xu, Tianyi Zang, Shangyang Li
Abstract
Computational mental health screening using multimodal speech and text has shown great promise. However, existing models often assume all clinical speech protocols carry equivalent evidentiary validity. In reality, heterogeneous protocols, from free interviews to fixed reading tasks, support fundamentally different evidence. Forcing uniform reasoning flattens these boundaries, causing models to hallucinate symptoms from irrelevant text or overclaim support. Even advanced long chain-of-thought LLMs fail to resolve this issue, as free-form reasoning can exacerbate boundary violations. To address this, we reformulate multimodal screening as an evidence-bounded reasoning problem. We introduce the Evidence Package Benchmark, integrating 1,870 packages across six heterogeneous sources with explicit modality masks and evidence permissions. We further propose EviBound, a protocol-aware evidence control framework. Unlike direct LLM prompting, EviBound uses a profile-aware planner to restrict reasoning scope, orchestrates evidence tools via five-way acoustic consensus, and enforces a boundary critic to suppress unsupported claims. Empirical results show EviBound achieves a held-out test Depression AUROC of 0.8658, exceeding the strongest direct omni-modal baseline by +0.0811 AUROC while maintaining zero claim violations. Our work moves beyond unconstrained accuracy toward evidence-consistent, protocol-aware systems for safer clinical NLP research.