The ambiguous world of emotion representation

作     者：Sethu, Vidhyasaharan Provost, Emily Mower Epps, Julien Busso, Carlos Cummins, Nicholas Narayanan, Shrikanth 

作者机构：School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Erik Jonsson School of Engineering & Computer Science University of Texas at Dallas RichardsonTX75080 United States ZD.B Chair of Embedded Intelligence for Health Care Wellbeing University of Augsburg Augsburg Germany Signal Analysis and Interpretation Lab University of Southern California Los AngelesCA90089 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2019年

核心收录：

主　　题：Speech recognition 

摘      要：Artificial intelligence and machine learning systems have demonstrated huge improvements and human-level parity in a range of activities, including speech recognition, face recognition and speaker verification. However, these diverse tasks share a key commonality that is not true in affective computing: the ground truth information that is inferred can be unambiguously represented. This observation provides some hints as to why affective computing, despite having attracted the attention of researchers for years, may not still be considered a mature field of research. A key reason for this is the lack of a common mathematical framework to describe all the relevant elements of emotion representations. This paper proposes the AMBiguous Emotion Representation (AMBER) framework to address this deficiency. AMBER is a unified framework that explicitly describes categorical, numerical and ordinal representations of emotions, including time varying representations. In addition to explaining the core elements of AMBER, the paper also discusses how some of the commonly employed emotion representation schemes can be viewed through the AMBER framework, and concludes with a discussion of how the proposed framework can be used to reason about current and future affective computing systems. Copyright © 2019, The Authors. All rights reserved.