HOPE: Hierarchical Fusion for Optimized and Personality-Aware Estimation of Depression

摘要

Depression detection remains challenged by generalized modeling approaches that fail to account for individual heterogeneity. To address this, the Multimodal Personality-aware Depression Detection (MPDD) Challenge introduced personalized features into the modeling process, aiming to better capture individual variability. However, the baseline models still exhibit two critical limitations: the neglect of textual semantics embedded in audio, and inconsistent predictions for the same subject across tasks and samples. Motivated by these limitations, we introduce HOPE (Hierarchical fusion for Optimized and Personality-aware Estimation of Depression), a unified framework for consistent, subject-level depression estimation. HOPE first employs a Latent Semantic Projection (LSP) module to reconstruct textual semantics from audio features when transcripts are unavailable. It then introduces a consistency-aware integration mechanism that hierarchically fuses multi-branch predictions to resolve inter-task and inter-sample contradictions. HOPE achieved first place in the MPDD Challenge Young Track, demonstrating strong cross-modal learning capabilities and consistent, subject-level depression prediction.