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Verbal and Nonverbal Clues for Real-life Deception Detection

机译:言语和非言语线索用于现实生活中的欺骗检测

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Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the availability of data, and most of the research work to date has been conducted on acted or artificially collected data. The generated deception models are thus lacking real-world evidence. In this paper, we explore the use of multi-modal real-life data for the task of deception detection. We develop a new deception dataset consisting of videos from real-life scenarios, and build deception tools relying on verbal and nonverbal features. We achieve classification accuracies in the range of 77-82% when using a model that extracts and fuses features from the linguistic and visual modalities. We show that these results outperform the human capability of identifying deceit.
机译:欺骗检测已受到计算语言学,语音和多模式处理社区的越来越多的关注。这项任务中遇到的主要挑战之一是数据的可用性,迄今为止,大多数研究工作都是针对实际或人工收集的数据进行的。因此,生成的欺骗模型缺乏现实世界的证据。在本文中,我们探索了将多模式现实生活数据用于欺骗检测的任务。我们开发了一个由真实场景中的视频组成的新欺骗数据集,并基于语言和非语言特征构建了欺骗工具。当使用从语言和视觉模式中提取和融合特征的模型时,我们可以实现77-82%的分类精度。我们表明,这些结果优于人类识别欺骗的能力。

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