Emotion Recognition Through Analysis of Speech – A Review

Abstract

The feature extraction is very important for emotion recognition through speech. There are several approaches when dealing with emotion recognition. In this paper, we present different feature extraction approaches as well as different models used to differentiate between a neutral speech versus an emotional speech sample. This research is instrumental for the digitization and preservation of cultural heritage, as it allows us to capture and analyze the emotional nuances in historical audio recordings, ensuring their accurate representation for future generations. We have selected two works consisting of a total of four different methods for emotion recognition. In the first paper by Jacob (2017), we look at Decision tree and Logistic Regression. Decision tree attains an 84.45% accuracy on the test class whereas logistic regression is able to achieve an accuracy of 66.85% after stepwise regression. These methods contribute to the digital archiving of cultural heritage by providing robust tools for analyzing and preserving the emotional content of spoken artifacts. In another paper by Bhatti et all. (2004), sequential forward selection (SFS) was used to create subsets from the given features and relevance of the subsets of features. General regression neural network was used to evaluate the accuracy which was found to be 80.69%. As a complementary purpose, modular neural network was performed with an accuracy of 83.31% with the same dataset. These techniques enhance our ability to maintain the integrity and emotional depth of cultural heritage recordings in digital archives.