Supervised Text to Speech

Supervised text-to-speech (TTS) aims to synthesize high-quality speech from text data using machine learning, focusing on improving efficiency and realism. Current research emphasizes developing models that require less labeled training data (semi-supervised and minimally-supervised approaches), often employing diffusion models and vector quantization techniques to generate more natural and expressive speech. These advancements are significant because they reduce the substantial data requirements of traditional TTS systems, making high-quality speech synthesis more accessible and applicable to a wider range of languages and voices.