Is Deception Emotional? An Emotion-Driven Predictive Approach

Abstract

In this paper, we propose a method for automatically detecting deceptive speech by relying on predicted scores derived from emotion dimensions such as arousal, valence, regulation, and emotion categories.The scores are derived from task-dependent models trained on the GEMEP emotional speech database.Inputs from the INTERSPEECH 2016 Computational Paralinguistics Deception sub-challenge are processed to obtain predictions of emotion attributes and associated scores that are then used as features in detecting deception.We show that using the new emotion-related features, it is possible to improve upon the challenge baseline.