Speech Emotion Recognition System Using Deep Learning and WebBased Prediction Interface

Understanding human emotions from speech can greatly improve the efficacy of intelligent systems, which is why Speech Emotion Recognition (SER) has grown in importance as a research subject in the realm of human–computer interaction. Applications like virtual assistants, customer service analysis, mental health monitoring, and smart communication systems can all benefit from the capacity to automatically identify emotions from speech signals. However, because real-world audio recordings vary in tone, pitch, speaking style, and background noise, it is difficult to discern emotions from speech. A deep learning-based Speech Emotion Recognition system that recognises emotional states from speech audio data is presented in this study. In order to capture significant acoustic properties of the audio stream, the suggested system uses voice recordings that are saved in the.wav format and processes them using feature extraction algorithms. A deep learning model that can categorise several emotional states, including anger, happiness, sadness, and neutral speech, is then trained using these extracted features. Real-time emotion prediction is accomplished by saving and integrating the trained model into the system. The Django framework is used to develop the system as a web-based application with an interactive user interface in addition to the machine learning component. This interface allows users to upload speech audio samples and receive the trained deep learning model's anticipated emotional outcomes. While preserving dependable emotion detection capabilities, the integration of deep learning algorithms with a web-based platform enhances accessibility and usability. The suggested system successfully analyses speech signals and correctly recognises emotional expressions from audio data, according to experimental results. The created framework demonstrates how deep learning methods can enhance speech-based emotion recognition systems and can be expanded for practical uses in affective computing and intelligent communication systems.