EMODE: Dynamic Para-Semantic Experts for Emotion-Aware Speech Language Modeling
Large speech language models have demonstrated strong capabilities in unified cross-modal understanding and generation, yet paralinguistic cues, especially emotion, remain difficult to preserve. Existing systems typically rely on entangled acoustic representations, which allow the underlying language model to depend excessively on recovered lexical content instead of grounding its behavior in acoustic-prosodic evidence. We address this limitation with EMODE, an emotion-aware speech language model built around \textbf{Dynamic Para-Semantic Experts (DPSE)}. DPSE decomposes continuous speech features into semantic and paralinguistic pathways, routes them dynamically, and fuses them before in...
| Authors | Jianan Pan, Yiwen Gu, Xinze Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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