Cross Corpus Speech Emotion Recognition

Cross-corpus speech emotion recognition (SER) focuses on building models that accurately identify emotions in speech even when training and testing data come from different sources, a crucial challenge due to variations in speaker demographics, recording conditions, and annotation styles. Current research emphasizes developing robust transfer learning methods, often employing deep learning architectures like convolutional neural networks and transformers, along with techniques such as contrastive learning and domain adaptation to bridge the gap between corpora. These advancements are significant for improving the generalizability and reliability of SER systems, impacting applications in areas like mental health monitoring, human-computer interaction, and affective computing.