  There are two paradigms of emotion representation, categorical labeling and
dimensional description in continuous space. Therefore, the emotion recognition
task can be treated as a classification or regression. The main aim of this
study is to investigate the relation between these two representations and
propose a classification pipeline that uses only dimensional annotation. The
proposed approach contains a regressor model which is trained to predict a
vector of continuous values in dimensional representation for given speech
audio. The output of this model can be interpreted as an emotional category
using a mapping algorithm. We investigated the performances of a combination of
three feature extractors, three neural network architectures, and three mapping
algorithms on two different corpora. Our study shows the advantages and
limitations of the classification via regression approach.
