Speech Emotion Recognition Using Convolutional Neural Network (CNN) - Singh, Apoorv; Srivastava, Kshitij Kumar; Murugan, Harini (International Journal of Psychosocial Rehabilitation, 2020)
The Automated Speech Emotion Recognition is a tough process because of the gap among acoustic characteristics and human emotions, which depends strongly on the discriminative acoustic characteristics extracted for a provided recognition task. Different persons have different emotions and altogether a different way to express it. Speech emotion do have different energies, pitch variations are emphasized if considering different subjects. Therefore, the speech emotion detection is a demanding task in computing vision. Here, the speech emotion recognition is based on the Convolutional Neural Network (CNN) algorithm which uses different modules for the emotion recognition and the classifiers are used to differentiate emotions such as happiness, surprise, anger, neutral state, sadness, etc. The dataset for the speech emotion recognition system is the speech samples and the characteristics are extracted from these speech samples using LIBROSA package. The classification performance is based on extracted characteristics. Finally we can determine the emotion of speech signal.