Is Deception Emotional? An Emotion-Driven Predictive Approach

Shahin Amiriparian, Jouni Pohjalainen, Erik Marchi, Sergey Pugachevskiy, Björn Schuller

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.

DOI: 10.21437/Interspeech.2016-565

Cite as

Amiriparian, S., Pohjalainen, J., Marchi, E., Pugachevskiy, S., Schuller, B. (2016) Is Deception Emotional? An Emotion-Driven Predictive Approach. Proc. Interspeech 2016, 2011-2015.

author={Shahin Amiriparian and Jouni Pohjalainen and Erik Marchi and Sergey Pugachevskiy and Björn Schuller},
title={Is Deception Emotional? An Emotion-Driven Predictive Approach},
booktitle={Interspeech 2016},