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Detecting deception using critical segments

Frank Enos, Elizabeth Shriberg, Martin Graciarena, Julia Hirschberg, Andreas Stolcke

We present an investigation of segments that map to global lies, that is, the intent to deceive with respect to salient topics of the discourse. We propose that identifying the truth or falsity of these

critical segments may be important in determining a speaker's veracity over the larger topic of discourse. Further, answers to key questions, which can be identified a priori, may represent emotional and cognitive

hot spots, analogous to those observed by psychologists who study gestural and facial cues to deception. We present results of experiments that use two different definitions of critical segments and employ machine learning techniques that compensate for imbalances in the dataset. Using this approach, we achieve a performance gain of 23.8% relative to chance, in contrast with human performance on a similar task, which averages substantially below chance. We discuss the features used by the models, and consider how these findings can influence future research.