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We present work on deception detection, where, given a spoken claim, we aimto predict its factuality. While previous work in the speech community hasrelied on recordings from staged setups where people were asked to tell thetruth or to lie and their statements were recorded, here we use real-worldpolitical debates. Thanks to the efforts of fact-checking organizations, it ispossible to obtain annotations for statements in the context of a politicaldiscourse as true, half-true, or false. Starting with such data from theCLEF-2018 CheckThat! Lab, which was limited to text, we performed alignment tothe corresponding videos, thus producing a multimodal dataset. We furtherdeveloped a multimodal deep-learning architecture for the task of deceptiondetection, which yielded sizable improvements over the state of the art for theCLEF-2018 Lab task 2. Our experiments show that the use of the acoustic signalconsistently helped to improve the performance compared to using textual andmetadata features only, based on several different evaluation measures. Werelease the new dataset to the research community, hoping to help advance theoverall field of multimodal deception detection.

Detecting Deception in Political Debates Using Acoustic and Textual Features