  We investigate the effect and usefulness of spontaneity (i.e. whether a given
speech is spontaneous or not) in speech in the context of emotion recognition.
We hypothesize that emotional content in speech is interrelated with its
spontaneity, and use spontaneity classification as an auxiliary task to the
problem of emotion recognition. We propose two supervised learning settings
that utilize spontaneity to improve speech emotion recognition: a hierarchical
model that performs spontaneity detection before performing emotion
recognition, and a multitask learning model that jointly learns to recognize
both spontaneity and emotion. Through various experiments on the well known
IEMOCAP database, we show that by using spontaneity detection as an additional
task, significant improvement can be achieved over emotion recognition systems
that are unaware of spontaneity. We achieve state-of-the-art emotion
recognition accuracy (4-class, 69.1%) on the IEMOCAP database outperforming
several relevant and competitive baselines.
