A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction

• URL: http://arxiv.org/abs/2601.04960v1
• Date: Thu, 08 Jan 2026 14:07:30 GMT
• Title: A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction
• Authors: Qing Wang, Zehan Li, Yaodong Song, Hongjie Chen, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Xuelong Li,
• Abstract summary: This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
• Score: 50.05919688888947
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT). IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision. The model is trained with a two-stage progressive strategy. The first stage performs speech-text alignment and emotional attribute modeling via self-distillation, while the second stage conducts end-to-end cross-modal joint optimization to ensure consistency between textual and spoken emotional expressions. Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation under both LLM-based and human evaluations.

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