Neural Tt

Neural text-to-speech (TTS) research focuses on creating high-quality, natural-sounding synthetic speech using neural networks. Current efforts concentrate on improving control over prosody and emotion, adapting models to new speakers with minimal data, and enhancing performance for low-resource languages through techniques like vector quantization and normalizing flows. These advancements leverage architectures such as neural HMMs and autoregressive models, aiming to produce more expressive, natural, and versatile synthetic speech. The resulting improvements have significant implications for applications ranging from accessibility technologies to virtual assistants and interactive storytelling.