Decoding Emotions: A Deep Learning Framework for Speech Emotion Recognition

Speech Emotion Recognition (SER) has become a very significant research field, with applications from human-computer interaction, mental health monitoring, and real-time communication systems. In this article, the diversity in methodologies applied in SER will be systematically reviewed while representing both conventional machine learning models, such as Support Vector Machines (SVM), and more complex deep learning architectures, including Long Short-Term Memory (LSTM) networks, attention-based models, and graph-based techniques. The functions of convolutional neural networks with LSTM and self-attention mechanisms are capturing spatial and temporal features significantly responsible for improving the detection against complex emotional states like anger, fear, and sadness. Among the feature extraction techniques critical to model performance, MFCCs and prosodic features have been more salient. While attention-based deep learning models demonstrate reliabilities of as high as 90%, traditional methods still provide faster real-time detection and are thus suitable for applications in domains such as telemedicine and online learning. Yet, the challenge of recognition with noise and complexity in real-world environments is not solved. Methods proposed include data augmentation, multi-lingual, and cross-corpus training for model robustness improvement. Its ongoing refinements, particularly adoption of attention mechanisms and of complex feature extraction, will be more important for higher accuracy and applicability across a wide range of real-world scenarios.