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Emotion plays a key role in many applications like healthcare, to gatherpatients emotional behavior. There are certain emotions which are given moreimportance due to their effectiveness in understanding human feelings. In thispaper, we propose an approach that models human stress from audio signals. Theresearch challenge in speech emotion detection is defining the very meaning ofstress and being able to categorize it in a precise manner. Supervised MachineLearning models, including state of the art Deep Learning classificationmethods, rely on the availability of clean and labelled data. One of theproblems in affective computation and emotion detection is the limited amountof annotated data of stress. The existing labelled stress emotion datasets arehighly subjective to the perception of the annotator.  We address the first issue of feature selection by exploiting the use oftraditional MFCC features in Convolutional Neural Network. Our experiments showthat Emo-CNN consistently and significantly outperforms the popular existingmethods over multiple datasets. It achieves 90.2% categorical accuracy on theEmo-DB dataset. To tackle the second and the more significant problem ofsubjectivity in stress labels, we use Lovheim's cube, which is a 3-dimensionalprojection of emotions. The cube aims at explaining the relationship betweenthese neurotransmitters and the positions of emotions in 3D space. The learntemotion representations from the Emo-CNN are mapped to the cube using threecomponent PCA (Principal Component Analysis) which is then used to model humanstress. This proposed approach not only circumvents the need for labelledstress data but also complies with the psychological theory of emotions givenby Lovheim's cube. We believe that this work is the first step towards creatinga connection between Artificial Intelligence and the chemistry of humanemotions.

Emo-CNN for Perceiving Stress from Audio Signals: A Brain Chemistry Approach