Prosody Modeling

Prosody modeling in speech synthesis aims to generate speech with natural intonation, rhythm, and stress, enhancing the expressiveness and naturalness of synthetic voices. Current research focuses on improving prosody control through various techniques, including reinforcement learning, diffusion models, and hierarchical architectures that leverage both global and local prosodic features, often incorporating linguistic information like syntax and phoneme-level details. These advancements are crucial for creating more human-like synthetic speech, impacting applications such as text-to-speech systems, voice assistants, and expressive speech synthesis for various languages and speakers. Furthermore, efficient automatic prosody annotation methods are being developed to reduce the reliance on expensive manual labeling.