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首页> 外文期刊>International journal of synthetic emotions >Advocating a Componential Appraisal Model to Guide Emotion Recognition
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Advocating a Componential Appraisal Model to Guide Emotion Recognition

机译：倡导成分评估模型指导情绪识别

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摘要

Most models of automatic emotion recognition use a discrete perspective and a black-box approach, i.e., they output an emotion label chosen from a limited pool of candidate terms, on the basis of purely statistical methods. Although these models are successful in emotion classification, a number of practical and theoretical drawbacks limit the range of possible applications. In this paper, the authors suggest the adoption of an appraisal perspective in modeling emotion recognition. The authors propose to use appraisals as an intermediate layer between expressive features (input) and emotion labeling (output). The model would then be made of two parts: first, expressive features would be used to estimate appraisals; second, resulting appraisals would be used to predict an emotion label. While the second part of the model has already been the object of several studies, the first is unexplored. The authors argue that this model should be built on the basis of both theoretical predictions and empirical results about the link between specific appraisals and expressive features. For this purpose, the authors suggest to use the component process model of emotion, which includes detailed predictions of efferent effects of appraisals on facial expression, voice, and body movements.
机译：自动情感识别的大多数模型都使用离散视角和黑盒方法，即，它们基于纯粹的统计方法输出从有限的候选术语库中选择的情感标签。尽管这些模型在情感分类中很成功，但是许多实际和理论上的缺陷限制了可能的应用范围。在本文中，作者建议在评估情绪识别时采用评估视角。作者建议将评估用作表达特征（输入）和情感标签（输出）之间的中间层。然后，该模型将由两部分组成：首先，将使用表达特征来评估评估；其次，评估结果将用于预测情感标签。尽管该模型的第二部分已经成为一些研究的对象，但第一部分尚未探索。作者认为，该模型应基于理论评估和有关具体评估与表达特征之间联系的实证结果的基础上建立。为此，作者建议使用情绪的组成过程模型，其中包括评估对面部表情，声音和身体运动的传出效果的详细预测。

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