Deep Learning-Based Recognizing and Visualizing Emotions Through Acoustic Signals

This study proposes a new technique for analyzing and visualizing emotions in speech by integrating color representation and intonation information to enhance text expression. A sentiment analysis system was developed by employing deep learning methods, particularly a IDCNN model, to integrate emotion analysis and text mapping. Through this, emotions were visually represented in text by size and color, providing an effective means of expressing emotions compared to conventional methods. This approach enables sentiment content language analysis applicable to various domains such as movies, dramas, computer games, etc. The combination of speech and color in emotion representation serves a universal role in conveying emotions transcending language barriers. Additionally, emotion color cards derived from this approach can be highly beneficial in educational environments, facilitating communication for students with hearing impairments or emotional developmental disorders. Moreover, it can assist in promptly identifying and blocking harmful content for children and adolescents.