EAGER: Physio-linguistic Models of Deception Detection

EAGER：欺骗检测的生理语言模型

• 批准号: 1355633
• 负责人: Mihai Burzo
• 金额: $ 30万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1355633&HistoricalAwards=false
• 关键词: EAGER; Physio; linguistic; Models; Deception

The goal of this Early-concept Grant for Exploratory Research is to explore a new generation of computational tools for joint modeling of physiological and linguistic signals of human behavior. The project is the first to investigate physio-linguistic models for deception analysis. To achieve this goal, the following three research objectives are pursued. First, a novel physio-linguistic dataset of deceit is built, covering several different domains. Second, rule-based classifiers for deception detection are explored, using physiological features (e.g., heart rate, respiration rate, galvanic skin response, skin temperature), as well as linguistic features. Third, data-driven learning approaches for multimodal deception detection are developed, taking advantage of the recent progress in early, late, and temporal fusion models. The project is exploratory in nature, and acts as a catalyst for novel research problems. First, it explores rich sets of multimodal features extracted from physiological and linguistic modalities, analyzing their effectiveness in the recognition of deceit. Second, it also explores the integration of multiple physio-linguistic modalities, through experiments with rule-based and data-driven techniques that fuse multimodal features into joint deception analysis models. To address the challenges of multimodal research work, the team working on this project brings together experts from the fields of bio-sensors, computational linguistics, and physiology and behavioral sciences.The project has high potential payoffs, as models of deception detection have broad applicability, including: the development of critical tools for various applications in fields such as criminal justice, intelligence, and security; the enhancement of applications that can be negatively affected by the presence of deceit, such as opinion analysis or modeling of human communication; and a deeper understanding of fundamental aspects of human behavior, which can positively impact medical applications in psychiatry and psychology. The tools and datasets produced during this project will be made freely available for the research community.For further information see the project web site at: http://web.eecs.umich.edu/~mihalcea/deceptiondetection/

探索性研究早期概念补助金的目标是探索新一代的计算工具，用于人类行为的生理和语言信号的联合建模。 该项目是第一个调查欺骗分析的生理语言模型。为了实现这一目标，我们追求以下三个研究目标。首先，建立了一个新的欺骗生理语言数据集，涵盖了几个不同的领域。其次，探索用于欺骗检测的基于规则的分类器，使用生理特征（例如，心率、呼吸率、皮肤电反应、皮肤温度）以及语言特征。第三，数据驱动的学习方法多模态欺骗检测的发展，利用早期，晚期和时间融合模型的最新进展。 该项目是探索性的，并作为新的研究问题的催化剂。首先，它探讨了丰富的多模态特征集提取的生理和语言模态，分析其有效性识别欺骗。其次，它还探讨了多种生理语言模态的整合，通过基于规则和数据驱动的技术，融合多模态特征到联合欺骗分析模型的实验。为了应对多模态研究工作的挑战，该项目的团队汇集了来自生物传感器、计算语言学、生理学和行为科学领域的专家。该项目具有很高的潜在回报，因为欺骗检测模型具有广泛的适用性，包括：为刑事司法、情报和安全等领域的各种应用开发关键工具;增强可能受到欺骗存在的负面影响的应用程序，例如意见分析或人类沟通建模;以及更深入地了解人类行为的基本方面，这可能会对精神病学和心理学的医学应用产生积极影响。 在这个项目期间产生的工具和数据集将免费提供给研究界。欲了解更多信息，请访问项目网站：http://web.eecs.umich.edu/~mihalcea/deceptiondetection/