Expressive Speech

Expressive speech synthesis aims to generate speech that conveys not only linguistic content but also emotional nuances and stylistic variations, mirroring the richness of human communication. Current research focuses on improving the expressiveness of models, often employing techniques like diffusion models, variational autoencoders, and graph neural networks, and incorporating linguistic features (e.g., emphasis, semantics) to enhance control and naturalness. Advances in this field have significant implications for applications such as virtual assistants, audiobooks, and accessibility technologies, while also providing valuable insights into the computational modeling of human communication.