Diffusion Based Text

Diffusion-based models are revolutionizing text-to-speech (TTS) synthesis, offering high-quality, diverse audio generation even in zero-shot scenarios. Current research focuses on improving robustness, efficiency, and control over aspects like speaker identity, emotion, and editing capabilities, often employing techniques like latent diffusion models, classifier-free guidance, and reinforcement learning for fine-tuning. These advancements are significantly impacting the field by enabling more natural and expressive speech synthesis, personalized voice generation, and efficient audio editing, with applications ranging from personalized assistants to multimedia content creation.