Researchers in both psychology and computer science have suggested that modeling individual differences may improve the performance of automatic deception detection systems. In this study, we fuse a personality classification task with a deception classifier and evaluate various ways to combine the two tasks, either as a single network with shared layers, or by feeding personality labels into the deception classifier. We show that including personality recognition improves the performance of deception detection on the Columbia X-Cultural Deception (CXD) corpus by more than 6% relative, achieving new state-of-the-art results on classification of phrase-like units in this corpus.
Similarly, in REF , we showed that jointly learning deception and personality labels improved deception detection.
Deep Personality Recognition for Deception Detection
INTERSPEECH
Computer Science