Automatic Deceit Detection Through Multimodal Analysis of High-Stake Court-Trials

In this article we propose the use of convolutional self-attention for attention-based representation learning, while replacing traditional vectorization methods with a transformer as the backbone of our speech model for transfer learning within our automatic deceit detection framework. This design performs a multimodal data analysis and applies fusion to merge visual, vocal, and speech(textual) channels; reporting deceit predictions. Our experimental results show that the proposed architecture improves the state-of-the-art on the popular
Real-Life Trial (RLT)
dataset in terms of correct classification rate. To further assess the generalizability of our design, we experiment on the low-stakes
Box of Lies (BoL)
dataset and achieve state-of-the-art performance as well as providing cross-corpus comparisons. Following our analysis, we report that (1) convolutional self-attention learns meaningful representations while performing joint attention computation for deception, (2) apparent deceptive intent is a continuous function of time and subjects can display varying levels of apparent deceptive intent throughout recordings, and (3), in support of criminal psychology findings, studying abnormal behavior out of context can be an unreliable way to predict deceptive intent.