Lingo-Aura: A Cognitive-Informed and Numerically Robust Multimodal Framework for Predictive Affective Computing in Clinical Diagnostics

Accurate assessment of emotional states is critical in clinical diagnostics, yet traditional multimodal sentiment analysis often suffers from "modality laziness," where models overlook subtle micro-expressions in favor of text priors. This study proposes Lingo-Aura, a cognitive-enhanced framework based on Mistral-7B designed to align visual micro-expressions and acoustic signals with large language model (LLM) embeddings. We introduce a robust Double-MLP Projector and global mean pooling to bridge the modality gap while suppressing temporal noise and ensuring numerical stability during mixed-precision training. Crucially, the framework leverages a teacher LLM to generate meta-cognitive label, such as reasoning mode and information stance, which are injected as explicit context to guide deep intent reasoning. Experimental results on the CMU-MOSEI dataset demonstrate that Lingo-Aura achieves a 135% improvement in emotion intensity correlation compared to text-only baselines. These findings suggest that Lingo-Aura effectively identifies discrepancies between verbal statements and internal emotional states, offering a powerful tool for mental health screening and pain assessment in non-verbal clinical populations.