  Effectively recognising and applying emotions to interactions is a highly
desirable trait for social robots. Implicitly understanding how subjects
experience different kinds of actions and objects in the world is crucial for
natural HRI interactions, with the possibility to perform positive actions and
avoid negative actions. In this paper, we utilize the NICO robot's appearance
and capabilities to give the NICO the ability to model a coherent affective
association between a perceived auditory stimulus and a temporally asynchronous
emotion expression. This is done by combining evaluations of emotional valence
from vision and language. NICO uses this information to make decisions about
when to extend conversations in order to accrue more affective information if
the representation of the association is not coherent. Our primary contribution
is providing a NICO robot with the ability to learn the affective associations
between a perceived auditory stimulus and an emotional expression. NICO is able
to do this for both individual subjects and specific stimuli, with the aid of
an emotion-driven dialogue system that rectifies emotional expression
incoherences. The robot is then able to use this information to determine a
subject's enjoyment of perceived auditory stimuli in a real HRI scenario.
