用动态模糊度建模情绪变化，提升连续情绪识别效果。
Emotions as Ambiguity-aware Ordinal Representations
- 以模糊度变化率建模情绪的动态模糊性
- 在无界情绪标签上达到最高CCC和SDA分数
- 适合关注情绪演化过程的研究者
情绪是固有模糊且动态的现象，但现有连续情绪识别方法要么忽略模糊性，要么将其视为静态变量。本文提出一种新的模糊感知序数情绪表征框架，同时捕捉情绪标注中的模糊性与情绪轨迹的时间动态性。具体地，通过模糊度的变化率来建模情绪模糊性。在RECOLA和GameVibe两个情感语料库上评估，针对有界（唤醒度、效价）和无界（参与度）连续轨迹进行测试。结果表明，序数表示在无界标签上优于传统模糊感知模型，取得最高的一致性相关系数（CCC）和符号差分一致性（SDA）得分，凸显其对轨迹动态性的建模能力；在有界轨迹上，序数表示在SDA上表现更优，展现出对标注情绪轨迹相对变化的更好捕捉能力。
原文摘要 · Abstract (English)
Emotions are inherently ambiguous and dynamic phenomena, yet existing continuous emotion recognition approaches either ignore their ambiguity or treat ambiguity as an independent and static variable over time. Motivated by this gap in the literature, in this paper we introduce ambiguity-aware ordinal emotion representations, a novel framework that captures both the ambiguity present in emotion annotation and the inherent temporal dynamics of emotional traces. Specifically, we propose approaches that model emotion ambiguity through its rate of change. We evaluate our framework on two affective corpora -- RECOLA and GameVibe -- testing our proposed approaches on both bounded (arousal, valence) and unbounded (engagement) continuous traces. Our results demonstrate that ordinal representations outperform conventional ambiguity-aware models on unbounded labels, achieving the highest Concordance Correlation Coefficient (CCC) and Signed Differential Agreement (SDA) scores, highlighting their effectiveness in modeling the traces' dynamics. For bounded traces, ordinal representations excel in SDA, revealing their superior ability to capture relative changes of annotated emotion traces.
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