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Exploring Perception Uncertainty for Emotion Recognition in Dyadic Conversation and Music Listening
Cognitive Computation ( IF 5.4 ) Pub Date : 2020-06-09 , DOI: 10.1007/s12559-019-09694-4
Jing Han , Zixing Zhang , Zhao Ren , Björn Schuller

Predicting emotions automatically is an active field of research in affective computing. Considering the property of the individual’s subjectivity, the label of an emotional instance is usually created based on opinions from multiple annotators. That is, the labelled instance is often accompanied with the corresponding inter-rater disagreement information, which we call here the perception uncertainty. Such uncertainty information, as shown in previous studies, can provide supplementary information for better recognition performance in such a subjective task. In this paper, we propose a multi-task learning framework to leverage the knowledge of perception uncertainty to ameliorate the prediction performance. In particular, in our novel framework, the perception uncertainty is exploited in an explicit manner to manipulate an initial prediction dynamically, in contrast to merely estimating the emotional state and perception uncertainty simultaneously, as done in a conventional multi-task learning framework. To evaluate the feasibility and effectiveness of the proposed method, we perform extensive experiments for time- and value-continuous emotion predictions in audiovisual conversation and music listening scenarios. Compared with other state-of-the-art approaches, our approach yields remarkable performance improvements in both datasets. The obtained results indicate that integrating the perception uncertainty information can enhance the learning process.



中文翻译:

探索二元对话和音乐听力中情感识别的感知不确定性

自动预测情绪是情感计算研究的活跃领域。考虑到个人主观性的属性,通常基于多个注释者的意见来创建情感实例的标签。也就是说,带标签的实例通常会伴随相应的评分者之间的异议信息,我们在此将其称为感知不确定性。如先前的研究所示,这种不确定性信息可以提供补充信息,以便在这样的主观任务中获得更好的识别性能。在本文中,我们提出了一个多任务学习框架,以利用感知不确定性的知识来改善预测性能。特别是在我们新颖的框架中 与仅在常规多任务学习框架中同时估计情绪状态和感知不确定性相比,以显式方式利用感知不确定性来动态地操纵初始预测。为了评估该方法的可行性和有效性,我们在视听会话和音乐收听场景中进行了时间和价值连续情感预测的广泛实验。与其他最新方法相比,我们的方法在两个数据集上均产生了显着的性能改进。获得的结果表明,整合感知不确定性信息可以增强学习过程。就像在传统的多任务学习框架中所做的那样。为了评估该方法的可行性和有效性,我们在视听会话和音乐收听场景中进行了时间和价值连续情感预测的广泛实验。与其他最新方法相比,我们的方法在两个数据集上均产生了显着的性能改进。获得的结果表明,整合感知不确定性信息可以增强学习过程。就像在传统的多任务学习框架中所做的那样。为了评估该方法的可行性和有效性,我们在视听会话和音乐收听场景中进行了时间和价值连续情感预测的广泛实验。与其他最新方法相比,我们的方法在两个数据集上均产生了显着的性能改进。获得的结果表明,整合感知不确定性信息可以增强学习过程。我们的方法在两个数据集上均产生了显着的性能改进。获得的结果表明,整合感知不确定性信息可以增强学习过程。我们的方法在两个数据集上均产生了显着的性能改进。获得的结果表明,整合感知不确定性信息可以增强学习过程。

更新日期:2020-06-09
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