부산대학교 도서관

In this article, we present a new method for understanding human emotions by analyzing speech using neural networks. Our method uses the power of deep learning to derive features from raw speech data without the need for artificial intelligence. We evaluate our approach based on big data and achieve the best performance in case of knowledge for basic needs. Our results show that the deep learning process can remove distinguishing features from speech to cognitive recognition and improve the traditional process. This article contributes to the study of cognitive psychology through discourse analysis and demonstrates the potential of deep learning in this field. Speech is a powerful tool for communicating emotions, and understanding people's emotions by analyzing speech can have important applications in many areas. In this article, we propose a method for combining speech and data for cognitive recognition. Our approach involves extracting text from speech data using natural language processing techniques and combining it with acoustic features. We then use a deep neural network model to classify emotions. We evaluated our method on a large dataset and achieved 83% accuracy in identifying six features. Our results show the effectiveness of communication and information in cognitive theoryand the potential of linguistic techniques in thisfield.