A Review on Speech Emotion Classification using Linear Predictive Coding and Neural Networks

Abstract

Abstract: Speech is one of the significant method for communicating their thoughts for human beings. Using the intelligence of computing devices to understand human emotions from language has become an interesting area. In the recent years this topic has grabbed so much attention. Speech emotion recognition (SER) has made significant paces with the evolution of hardware and software systems in digital signal processing field. SER is a key feature of human-computer interaction systems that are mostly employed in various fields such as healthcare, automated call centres, and distance learning. SER comprises of the depth study about the signal and also recognizing the appropriate emotions in relation to the pre-determined dataset using the extracted features. The proposed system consists of speech signal, feature extraction module, pre-determined dataset, classifier, and lastly the classified emotions. Initially, the speech signal will be pre-processed which helps to eliminate the unwanted noise signal present in the speech signal. This pre-processed signal will be sent to the feature extraction module for the extraction of different types of features contained in a speech. Lastly, mapping only the required features corresponding to different types of emotionsis done. This mapping is done by using classifiers. In SER, several methods are employed to obtain the different types of emotions present in speech signals. Recently, Deep learning techniques forSER were offered as an alternative to traditional methods