Empirical Analysis of Shallow and Deep Architecture Classifiers on Emotion Recognition from Speech

Emotion detection or recognition from speech is currently a very crucial area of research with a plethora of applications in day to day life. Human communication depends heavily on mood, emotions and feelings. Availability of advanced signal processing techniques and artificial intelligence techniques like machine learning architecture (shallow classifiers) and neural network architecture (deep classifiers) have made this domain a booming area of research with increased efficiency and accuracy. This paper aims to empirically analyze various statistical machine learning algorithms like Naive Bayes, Support Vector Machine, Random Forest and deep learning algorithms like Convolutional Neural Network, Long Short Term Memory over emodb dataset which is publicly available for emotion classification into angry, sad, happy, neutral, other classes. A comparison of shallow classifiers on the basis of accuracy will help future researchers in providing hindsight into the field of emotion detection. Same goes for the comparison between the deep learning techniques.