BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning

This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition
(EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network
(DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by
using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition
rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech
signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional
features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train
the DNN model. Finally, a trained model that could recognize the emotional sig