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ReH-FUSE is proposed, a reliability-aware framework with dialogue-aware text, audio, and cross-modal experts, whose decision-level router first models the relative preference between text and audio and then balances the resulting unimodal mixture against the cross-modal expert.
DSSM-CRF, an audio-only architecture that explicitly separates cross-speaker contextual influence and within-speaker emotion evolution, is proposed and matched controls demonstrate complementary gains from speaker-wise factorization and CRF modeling.
This work presents a protocol-aware empirical assessment across three settings: a C-MAPSS degradation-risk proxy, normal-only training for anomalous-sound detection on MIMII, and BDG2 forecasting-residual diagnostics with synthetic target perturbations.
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