Emotional Speech

Emotional speech research focuses on automatically recognizing and synthesizing human emotions from spoken language, aiming to improve human-computer interaction and various applications requiring emotional intelligence. Current research emphasizes developing robust models, often employing deep learning architectures like convolutional neural networks (CNNs), transformers, and diffusion models, to handle the complexities of emotional expression in speech, including cross-domain learning from music and addressing challenges like noise and data scarcity. This field is significant for advancing our understanding of human emotion and its acoustic manifestations, with potential impacts on mental health assessment, customer service, and the development of more empathetic and natural-sounding AI systems.