Is deception emotional? An emotion-driven predictive approach

被引：21

作者：

Amiriparian, Shahin ^{[1
,2
]}

Pohjalainen, Jouni ^{[1
]}

Marchi, Erik ^{[1
]}

Pugachevskiy, Sergey ^{[1
]}

Schuller, Bjorn ^{[1
,3
]}

机构：

[1] Univ Passau, Chair Complex & Intelligent Syst, Passau, Germany

[2] Tech Univ Munich, Machine Intelligence & Signal Proc Grp, Munich, Germany

[3] Imperial Coll London, Machine Learning Grp, London, England

来源：

17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5: UNDERSTANDING SPEECH PROCESSING IN HUMANS AND MACHINES | 2016年

关键词：

computational paralinguistics; emotion; deception; DETECTING DECEPTION; CLASSIFICATION; AROUSAL; SPEECH;

D O I：

10.21437/Interspeech.2016-565

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：2011 / 2015

页数：5

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