Natural Sounding Speech

Natural-sounding speech synthesis aims to generate human-like speech from text, focusing on improving quality, diversity, and robustness across languages and speaking styles. Current research emphasizes advancements in model architectures like diffusion models, variational autoencoders, and transformer networks, often incorporating techniques such as disentangled representations and adversarial training to enhance naturalness and control over prosody and emotion. This field is crucial for applications ranging from assistive technologies and personalized voice assistants to combating synthetic misinformation, driving ongoing efforts to develop more accurate and efficient speech synthesis systems and robust detection methods.