Emotional Analysis Through Deep Learning Models

Physical fatigue is a major contributor to emotional suffering. Recently, scholars have been interested in emotion analysis. The first stage involves researching and pre-processing the content of emotional fatigue. The findings of a survey of novel theories, methods, and tools for speech emotion analysis are presented in this work. It introduces the idea of emotional exhaustion and categories it. Then, utilising multimodal data, the system is configured to identify emotional tiredness. The approach for recognising emotions in different voices is suggested in this study and is based on deep learning. Using many channels of a recurrent neural network, the system extracts audio data components for emotional recognition. Finally, the multi-modal data components are merged for emotion prediction. The speech analysis techniques, datasets, and resources are compiled. These results could shed light on applications of emotional contact as network nationalism, social networking, and human-computer interaction.