Vocal Expression

Vocal expression research focuses on understanding how vocalizations—including speech, non-speech sounds, and even subtle variations in tone—convey information about health, emotion, and social interaction across species. Current research utilizes machine learning, particularly neural networks (like recurrent and convolutional architectures), and advanced signal processing techniques to analyze acoustic features and classify vocalizations for applications ranging from disease diagnosis to human-computer interaction. This field is rapidly advancing, driven by the development of large, annotated datasets and the increasing sophistication of computational models, with significant implications for healthcare, animal behavior studies, and the design of more intuitive human-machine interfaces.