Defining Emotionally Salient Regions Using Qualitative Agreement Method
Defining Emotionally Salient Regions Using Qualitative Agreement Method
复制标题
使用定性一致性方法定义情感显着区域
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
C. Busso
中科院分区：
文献类型：
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作者：
Srinivas Parthasarathy;C. Busso
传统的情感分类方法集中在预定义的片段，如句子或说话轮，标记和分类在片段级别。然而，情感状态在人类互动过程中动态波动，因此并非所有片段都具有相同的相关性。我们感兴趣的是检测互动中情绪特别突出的区域，我们称之为情绪热点。具有这种能力的系统可以在许多领域中具有真实的应用。构建这样一个系统的关键一步是定义可靠的热点标签，这将决定机器学习算法的性能。从头开始创建groundtruth标签既昂贵又耗时。本文还表明，定义这些情绪显着的部分使用知觉评价是一个很难的问题，导致低评价者之间的协议。相反，我们建议利用现有的时间连续的情感标签来定义情感显著区域。所提出的方法依赖于定性协议（QA）的方法，它动态地捕捉由多个评估者提供的情感轨迹的增加或减少的趋势。所提出的方法是更可靠的，而不仅仅是平均跨评估的痕迹，提供了灵活性，以定义热点在不同的可靠性水平，而不必重新选择新的感知评估。
Conventional emotion classification methods focus on predefined segments such as sentences or speaking turns that are labeled and classified at the segment level. However, the emotional state dynamically fluctuates during human interactions, so not all the segments have the same relevance. We are interested in detecting regions within the interaction where the emotions are particularly salient, which we refer to as emotional hotspots. A system with this capability can have real applications in many domains. A key step towards building such a system is to define reliable hotspot labels, which will dictate the performance of machine learning algorithms. Creating groundtruth labels from scratch is both expensive and time consuming. This paper also demonstrates that defining those emotionally salient segments using perceptual evaluation is a hard problem resulting in low inter-evaluator agreement. Instead, we propose to define emotionally salient regions leveraging existing time-continuous emotional labels. The proposed approach relies on the qualitative agreement (QA) method, which dynamically captures increasing or decreasing trends across emotional traces provided by multiple evaluators. The proposed method is more reliable than just averaging traces across evaluators, providing the flexibility to define hotspots at various reliability levels without having to recollect new perceptual evaluations.