EmotionThinker: 基于语调感知的强化学习用于可解释的语音情绪推理
EmotionThinker: Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning
AI总结：
本文提出EmotionThinker，通过强化学习将语音情绪识别转化为深度推理问题，利用细粒度声学线索生成可解释的情绪预测，改进了语音大语言模型的语调感知能力。
摘要：
语音中的情感信息在多模态感知中具有独特作用。然而，当前的语音大语言模型（SpeechLLMs）仍将情绪理解视为简单的分类问题，限制了预测的可解释性，并未充分利用LLMs的表达和推理能力。本文首次通过强化学习将SER重新表述为深度推理问题。我们提出EmotionThinker，旨在生成准确的情绪预测并基于细粒度声学线索生成可解释的解释。为此，我们首先构建了具有链式推理注释和详细描述的EmotionCoT-35K情感推理数据集。其次，我们发现当前SpeechLLMs在语调感知方面表现较弱，而语调线索是解读情感的基本信号。为此，我们开发了增强语调的基础模型EmotionThinker-Base，并证明语调增强提高了情绪理解。第三，我们引入了具有逐步信任意识推理奖励的组相对策略优化（GRPO-PTR）进行强化学习。与标准GRPO不同，GRPO-PTR逐步引入推理奖励，动态调整奖励权重以反映推理与结果的一致性，并通过基于多维标准的奖励模型评估整体推理质量。EmotionThinker在情绪准确性和解释质量上均优于先前最先进的评估模型，推动SER向可解释的多模态推理发展。项目页面：https://github.com/dingdongwang/EmotionThinker
英文摘要：
Emotional information in speech plays a unique role in multimodal perception. However, current Speech Large Language Models (SpeechLLMs), similar to conventional speech emotion recognition (SER) systems, still treat emotion understanding as a simple classification problem. This provides limited interpretability of predictions, while leaving the LLMs' expressive and reasoning capabilities underutilized. In this work, we take the first step to reformulate SER as a deep reasoning problem through reinforcement learning (RL). We propose EmotionThinker, which is designed to generate accurate emotion predictions with interpretable explanations grounded in fine-grained acoustic cues. To achieve this, we first construct EmotionCoT-35K, an emotional reasoning dataset with Chain-of-Thought annotations and detailed captions. Second, we observe that current SpeechLLMs exhibit weak prosody perception, whereas prosodic cues constitute fundamental signals for interpreting emotions. To address this, we develop the prosody-enhanced foundation model EmotionThinker-Base, and demonstrate that prosody enhancement improves emotion understanding. Third, we introduce Group-Relative-Policy-Optimization with Progressive-Trust-aware-Reasoning-Reward (GRPO-PTR) for RL. Different from standard GRPO, which relies only on rule-based outcome rewards, GRPO-PTR progressively introduces reasoning reward, dynamically adjusts it with a trustworthiness weight reflecting the alignment between reasoning and outcome, and evaluates the overall reasoning quality with a reward model based on multi-dimensional criteria. EmotionThinker outperforms previous state-of-the-art evaluation models both in emotion accuracy and explanation quality, advancing SER toward interpretable multimodal reasoning. Project page: https://github.com/dingdongwang/EmotionThinker
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
