论文标题
使用深层神经网络将夹带与一致性脱钩
Decoupling entrainment from consistency using deep neural networks
论文作者
论文摘要
人类对话者倾向于从事称为夹带的自适应行为，从而变得更加相似。隔离一致性的效果，即遵守各自样式的说话者，是分析夹带的关键部分。我们建议将说话者的初始声音特征视为预测随后输出的混杂。使用两种现有的神经方法进行解污染，我们定义了控制一致性的新夹带措施。这些成功地将真实互动与假互动区分开。有趣的是，我们的更严格的方法与与以前不考虑一致性的措施相反的社会变量相关。这些结果证明了使用神经网络对夹带进行建模的优势，并提出了有关如何解释对话质量与不考虑一致性的夹带措施的先前关联的问题。
Human interlocutors tend to engage in adaptive behavior known as entrainment to become more similar to each other. Isolating the effect of consistency, i.e., speakers adhering to their individual styles, is a critical part of the analysis of entrainment. We propose to treat speakers' initial vocal features as confounds for the prediction of subsequent outputs. Using two existing neural approaches to deconfounding, we define new measures of entrainment that control for consistency. These successfully discriminate real interactions from fake ones. Interestingly, our stricter methods correlate with social variables in opposite direction from previous measures that do not account for consistency. These results demonstrate the advantages of using neural networks to model entrainment, and raise questions regarding how to interpret prior associations of conversation quality with entrainment measures that do not account for consistency.
