Sympatheia: 具有连续情感调节的情感自适应语音助手
Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning
AI总结：
提出Sympatheia语音对话框架，通过从用户语音推断情感并结合连续效价-唤醒度控制信号，实现情感自适应响应，优于基线模型。
摘要：
共情口语对话系统必须推断用户的情感状态以做出适当响应，然而日常语音通常带有微弱、中性或模糊的情感线索。为解决这一问题，我们引入了Sympatheia，一种语音到语音对话框架，其条件基于从用户语音中推断出的情感，并且在可用时，基于多模态感知模块或用户界面提供的连续效价-唤醒度（VA）控制信号中的明确情感规格。为了训练我们的模型，我们构建了Sympatheia-18k，一个包含12个情感锚点的情感条件合成口语对话语料库。该数据集包括用于学习情感语音行为的情感分割，以及一个中性分割，该分割将情感中性查询与多个情感条件响应配对，以在情感模糊情况下隔离明确的情感控制。实验结果表明，Sympatheia在生成语义内容和口语表达均情感适当的响应方面优于语音对话基线。我们进一步表明，相同的VA界面可以整合来自不同感知模块（包括面部表情、生物信号和文本情感描述）的情感估计，从而在语音单独提供有限情感证据时改善响应对齐。这些结果表明，连续情感调节是构建情感自适应语音助手的有效实际步骤。
英文摘要：
Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues. To address this, we introduce Sympatheia, a speech-to-speech dialogue framework conditioned on affect inferred from the user's speech and, when available, explicit affect specifications provided as a continuous valence--arousal (VA) control signal by a multimodal sensing module or user interface. To train our model, we construct Sympatheia-18k, an emotion-conditioned synthetic spoken dialogue corpus with 12 emotion anchors. This dataset includes an emotional split for learning affective speech behavior, and a neutral split that pairs emotionally neutral queries with multiple emotion-conditioned responses to isolate explicit emotion control in emotionally ambiguous cases. Empirical results show that Sympatheia outperforms speech conversational baselines in generating responses whose semantic content and spoken delivery are both emotionally appropriate. We further show that the same VA interface can integrate emotion estimates from diverse sensing modules, including facial expression, biosignals, and textual affect descriptions, improving response alignment when speech alone provides limited emotional evidence. These results suggest that continuous affect conditioning is an effective practical step for building emotionally adaptive voice assistants.
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。
