  Can vocal emotions be emulated? This question has been a recurrent concern of
the speech community, and has also been vigorously investigated. It has been
fueled further by its link to the issue of validity of acted emotion databases.
Much of the speech and vocal emotion research has relied on acted emotion
databases as valid proxies for studying natural emotions. To create models that
generalize to natural settings, it is crucial to work with valid prototypes --
ones that can be assumed to reliably represent natural emotions. More
concretely, it is important to study emulated emotions against natural emotions
in terms of their physiological, and psychological concomitants. In this paper,
we present an on-scale systematic study of the differences between natural and
acted vocal emotions. We use a self-attention based emotion classification
model to understand the phonetic bases of emotions by discovering the most
'attended' phonemes for each class of emotions. We then compare these
attended-phonemes in their importance and distribution across acted and natural
classes. Our tests show significant differences in the manner and choice of
phonemes in acted and natural speech, concluding moderate to low validity and
value in using acted speech databases for emotion classification tasks.

Audio and Speech Processing;Computation and Language;Machine Learning;Sound