High Fidelity Speech

High-fidelity speech synthesis aims to generate highly realistic and natural-sounding speech, focusing on improving both objective quality metrics and subjective listening experience. Current research heavily utilizes generative adversarial networks (GANs) and diffusion probabilistic models (DDPMs), often incorporating techniques like multi-scale analysis, time-frequency domain supervision, and adaptive noise shaping to enhance the generated audio. These advancements are driving significant improvements in speech super-resolution, vocoder performance, and text-to-speech systems, with implications for applications ranging from assistive technologies to virtual assistants and realistic audio-visual content creation.