Comparison of Various Feature Selection Algorithms in Speech Emotion Recognition

Abstract

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.