Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Speech Emotion Recognition (SER) plays a predominant role in human-machine interaction. SER is a challenging task because of number of complexities involved in it. For an accurate emotion classification system, feature extraction is the first and important step carried out on speech signals. And after the features are extracted, it is very important to select the best features out of all and reject the redundant and least important features. Feature selection methods play an important role in SER performance. The classifier gets the selected features, so as to reduce the unnecessary overload and perform better to classify the emotions. In this study, a good combination of features is selected from Punjabi Emotional Speech Database. Then a number of feature selection algorithms are explored and experimented upon, to select the best features. 1D-CNN is used for classification purpose. The results are shown and compared on the basis of number of performance metrics. LASSO has shown the best performance results as compared to other feature selection methods.