SPEECH EMOTION RECOGNITION

Abstract

Speech Emotion Recognition (SER) is an emerging area in artificial intelligence that focuses on identifying human emotions from speech signals using advanced computational techniques. This project presents the development of an efficient SER system that leverages machine learning and deep learning models to accurately detect emotions such as happiness, sadness, anger, fear, and neutrality from audio input. The system processes speech signals by extracting key features such as Mel Frequency Cepstral Coefficients (MFCC), pitch, and spectrograms, which play a vital role in capturing emotional patterns. These features are then utilized by a hybrid deep learning architecture combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to enhance classification accuracy and performance. The proposed system includes a user-friendly interface that enables users to upload or record speech and receive real-time emotion predictions in a clear and understandable format. It is designed to handle variations in speech, including differences in tone, accent, and background noise, ensuring robustness and reliability. The system has wide-ranging applications in human-computer interaction, virtual assistants, customer service automation, and mental health monitoring. By enabling machines to understand and interpret human emotions, the proposed solution enhances communication and interaction between humans and intelligent systems. Overall, this Speech Emotion Recognition system provides an accurate, scalable, and efficient approach to emotion detection, contributing to advancements in affective computing and intelligent technologies.