In many applications, Embodied Conversational Agents (ECAs) must be able to express various affects such as emotions or social attitudes. Non-verbal signals, such as smiles or gestures, contribute to the expression of attitudes. Social attitudes affect the whole behavior of a person: they are “characteristic of an affective style that colors the entire interaction” [1] . Moreover, recent findings have demonstrated that non-verbal signals are not interpreted in isolation but along with surrounding signals. Non-verbal behavior planning models designed to allow ECAs to express attitudes should thus consider complete sequences of non-verbal signals and not only signals independently of one another. However, existing models do not take this into account, or in a limited manner. The contribution of this paper is a methodology for the automatic extraction of sequences of non-verbal signals characteristic of a social phenomenon from a multimodal corpus, and a non-verbal behavior planning model that takes into account sequences of non-verbal signals rather than signals independently. This methodology is applied to design a virtual recruiter capable of expressing social attitudes, which is then evaluated in and out of an interaction context.

中文翻译：

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一种自动提取和生成传达人际态度的非语言信号序列的方法

在许多应用中，Embodied Conversational Agents (ECA) 必须能够表达各种影响，例如情绪或社会态度。非语言信号，例如微笑或手势，有助于表达态度。社会态度影响一个人的整个行为：它们是“影响整个互动的情感风格的特征”[1]。此外，最近的研究结果表明，非语言信号不是孤立地解释的，而是与周围的信号一起解释的。因此，旨在允许 ECA 表达态度的非语言行为规划模型应考虑非语言信号的完整序列，而不仅仅是相互独立的信号。但是，现有模型没有考虑到这一点，或者是以有限的方式。本文的贡献是一种从多模态语料库中自动提取具有社会现象特征的非语言信号序列的方法，以及一种考虑非语言信号序列而非信号序列的非语言行为规划模型。独立。这种方法被应用于设计一个能够表达社会态度的虚拟招聘人员，然后在交互环境中进行评估。