人类反馈驱动的动态语音情感识别
Human Feedback Driven Dynamic Speech Emotion Recognition
AI总结：
本文提出人类反馈驱动的动态语音情感识别方法，利用狄利克雷分布建模情感混合，并通过人类反馈迭代改进模型，实验证明其优于滑动窗口方法且标注更简化。
摘要：
本工作旨在探索动态语音情感识别这一新领域。与传统方法不同，我们假设每个音频片段都与一系列在不同时刻活跃的情感相关联。本研究特别关注情感3D虚拟形象的动画生成。我们提出了一种多阶段方法，包括训练经典语音情感识别模型、合成生成情感序列，以及基于人类反馈进一步改进模型。此外，我们引入了一种基于狄利克雷分布的情感混合建模新方法。模型基于从3D面部动画数据集中提取的真实情感标签进行评估。我们将所提模型与滑动窗口方法进行了比较。实验结果表明，基于狄利克雷分布的方法在情感混合建模中具有有效性。引入人类反馈进一步提升了模型质量，同时提供了一种简化的标注流程。
英文摘要：
This work proposes to explore a new area of dynamic speech emotion recognition. Unlike traditional methods, we assume that each audio track is associated with a sequence of emotions active at different moments in time. The study particularly focuses on the animation of emotional 3D avatars. We propose a multi-stage method that includes the training of a classical speech emotion recognition model, synthetic generation of emotional sequences, and further model improvement based on human feedback. Additionally, we introduce a novel approach to modeling emotional mixtures based on the Dirichlet distribution. The models are evaluated based on ground-truth emotions extracted from a dataset of 3D facial animations. We compare our models against the sliding window approach. Our experimental results show the effectiveness of Dirichlet-based approach in modeling emotional mixtures. Incorporating human feedback further improves the model quality while providing a simplified annotation procedure.
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。
