Analysis of Speech Emotion Recognition and Detection using Deep Learning

Speech emotion recognition (SER) is a mechanism to identify emotions from speech or voice. With the help of SER, robots can understand human feelings. In this challenging world, SER is one of the latest inventions that help to know about human emotion when saying words. Before the widespread use of deep learning, SER relied on various approaches, including support vector machines (SVM) and hidden Markov models (HMM) with several distinct and preprocessing technical features. SER is a challenging task of computational human interaction. This topic has gotten so much attention in the past couple of years. Numerous techniques have been used in speech emotion recognition to extract emotions from voice signals, including several well-developed speech examinations and classification methods. In the traditional way of speech emotion, recognition features are extracted from the speech signals. Then the features are selected, which is collectively known as the selection module, and then the emotions are recognized. This is a very lengthy and time taking process. This paper design an algorithm based on feature extraction and model creation that recognizes the emotion based on the deep learning technique.