  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.
