From Text to Speech: A Multimodal Cross-Domain Approach for Deception Detection
From Text to Speech: A Multimodal Cross-Domain Approach for Deception Detection
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从文本到语音：欺骗检测的多模态跨域方法
DOI：
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发表时间：
2018
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通讯作者：
H. Escalante
中科院分区：
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作者：
Rodrigo Rill;Luis Villaseñor;Verónica Reyes;H. Escalante
欺骗检测——识别某人何时试图让他人相信不实之事——对人类来说是一项艰巨的任务。对于自动方法而言，这项任务甚至更加困难，因为它们必须处理诸如缺乏足够的标记数据等额外问题。在这种情况下，跨领域分类形式的迁移学习是一项旨在利用某些有标记数据的领域的标记数据来帮助那些数据稀缺的其他领域的任务。本文对多模态数据跨领域欺骗检测的语言特征适用性进行了研究。具体而言，我们旨在学习不同书面文本领域（一种模态）的欺骗检测模型，并将新知识应用于从口语陈述转录的不相关主题（另一种模态）。实验结果表明，通过使用LIWC（语言调查和单词计数）和词性n元语法，我们在模态内达到了69.42%的准确率，以及0.7153的AUC ROC（受试者工作特征曲线下面积）。在进行迁移学习时，我们实现了63.64%的准确率，并获得了0.6351的AUC ROC。
Deception detection -identifying when someone is trying to cause someone else to believe something that is not true- is a hard task for humans. The task is even harder for automatic approaches, that must deal with additional problems like the lack of enough labeled data. In this context, transfer learning in the form of cross-domain classification is a task that aims to leverage labeled data from certain domains for which labeled data is available to others for which data is scarce. This paper presents a study on the suitability of linguistic features for cross-domain deception detection on multimodal data. Specifically, we aim to learn models for deception detection across different domains of written texts (one modality) and apply the new knowledge to unrelated topics transcribed from spoken statements (another modality). Experimental results reveal that by using LIWC and POS n-grams we reach a in-modality accuracy of 69.42%, as well as an AUC ROC of 0.7153. When doing transfer learning, we achieve an accuracy of 63.64% and get an AUC ROC of 0.6351.