"Is deception emotional? An emotion-driven predictive approach" at INTERSPEECH 2016
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.
Related Paper/Citation: Amiriparian, Shahin, Jouni Pohjalainen, Erik Marchi, Sergey Pugachevskiy, and Björn Schuller. "Is deception emotional? An emotion-driven predictive approach." in Interspeech, 2016, San Fransisco, USA, pp. 2011-2015.