TRACE: 双人语音中基于时间关系的对话韵律同步检测
TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech
AI总结：
提出TRACE框架，通过窗口级声学嵌入建模双人交互的时间序列，结合对话上下文和关系信息，在DyadEE数据集上实现97.01%的情感韵律同步检测准确率。
摘要：
随着语音AI代理的普及，理解对话交互中的情感韵律同步变得越来越重要。情感韵律同步由社会关系和对话语境塑造，影响随时间的情感协调。我们引入了DyadEE，一个用于双人语音交互中情感韵律同步检测的数据集，包含情感同步对话和通过伙伴交换及情感重合成破坏同步的合成交互。我们进一步提出了TRACE，一个窗口级框架，将双人交互建模为从情感微调Whisper表示中导出的声学嵌入的有序序列，将每个样本视为交互轨迹而非聚合话语。在DyadEE上的实验结果表明，结合对话上下文和关系信息改进了情感韵律同步检测，TRACE达到了97.01%的最佳准确率。
英文摘要：
With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important. Emotional entrainment is shaped by social relationships and conversational context, influencing affective coordination over time. We introduce DyadEE, a dataset for emotional entrainment detection in dyadic speech interactions, containing both emotionally entrained conversations and synthetic interactions where entrainment is disrupted through partner swapping and emotion resynthesis. We further propose TRACE, a window-level framework that models dyadic interaction as ordered sequences of acoustic embeddings derived from emotion fine-tuned Whisper representations, treating each sample as an interaction trace rather than pooled utterances. Experimental results on DyadEE show that incorporating conversational context and relationship information improves emotional entrainment detection, with TRACE achieving the best accuracy of 97.01%.
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。
