The Ambiguous World of Emotion Representation

TL;DR

This paper introduces the AMBER framework, a unified mathematical model for representing emotions in affective computing, addressing the lack of a common framework and enabling better reasoning about emotion representations.

Contribution

The paper proposes the AMBER framework, a comprehensive model that unifies categorical, numerical, and ordinal emotion representations, including dynamic aspects, to advance affective computing research.

Findings

AMBER provides a formal structure for emotion representations.

It unifies various existing emotion schemes under one framework.

The framework facilitates reasoning about emotion models and future systems.

Abstract

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.