  Emotion plays a key role in many applications like healthcare, to gather
patients emotional behavior. There are certain emotions which are given more
importance due to their effectiveness in understanding human feelings. In this
paper, we propose an approach that models human stress from audio signals. The
research challenge in speech emotion detection is defining the very meaning of
stress and being able to categorize it in a precise manner. Supervised Machine
Learning models, including state of the art Deep Learning classification
methods, rely on the availability of clean and labelled data. One of the
problems in affective computation and emotion detection is the limited amount
of annotated data of stress. The existing labelled stress emotion datasets are
highly subjective to the perception of the annotator.
  We address the first issue of feature selection by exploiting the use of
traditional MFCC features in Convolutional Neural Network. Our experiments show
that Emo-CNN consistently and significantly outperforms the popular existing
methods over multiple datasets. It achieves 90.2% categorical accuracy on the
Emo-DB dataset. To tackle the second and the more significant problem of
subjectivity in stress labels, we use Lovheim's cube, which is a 3-dimensional
projection of emotions. The cube aims at explaining the relationship between
these neurotransmitters and the positions of emotions in 3D space. The learnt
emotion representations from the Emo-CNN are mapped to the cube using three
component PCA (Principal Component Analysis) which is then used to model human
stress. This proposed approach not only circumvents the need for labelled
stress data but also complies with the psychological theory of emotions given
by Lovheim's cube. We believe that this work is the first step towards creating
a connection between Artificial Intelligence and the chemistry of human
emotions.
