Speech Emotion Recognition

Speech emotion recognition (SER) aims to automatically identify human emotions from speech, primarily focusing on improving accuracy and robustness across diverse languages and contexts. Current research emphasizes leveraging self-supervised learning models, particularly transformer-based architectures, and exploring techniques like cross-lingual adaptation, multi-modal fusion (combining speech with text or visual data), and efficient model compression for resource-constrained environments. Advances in SER have significant implications for various applications, including mental health monitoring, human-computer interaction, and personalized healthcare, by enabling more natural and empathetic interactions between humans and machines.