Tt Model

Text-to-speech (TTS) models aim to synthesize natural-sounding human speech from text input, a task increasingly tackled using deep learning. Current research focuses on improving speech quality and efficiency, exploring techniques like incorporating self-supervised learning for better speech representations, leveraging denoising diffusion probabilistic models for high-fidelity audio, and employing architectures that account for syntactic information and cross-sentence context for more natural prosody. These advancements are significant for both expanding low-resource language capabilities and enabling applications such as high-quality speech synthesis for assistive technologies and multimedia content creation.