#StyleTTS

Explore an innovative solution addressing text-to-speech synthesis challenges, emphasizing natural prosodic variations and diverse speaking styles. The style-based generative model incorporates the novel Transferable Monotonic Aligner (TMA) and duration-invariant data augmentation to surpass state-of-the-art performances. It facilitates self-supervised learning of speaking styles, enabling the generation of varied speech with precise prosody and emotional tones without explicit categorization. This advanced TTS model enhances naturalness and similarity across single and multi-speaker datasets, promoting efficient speech synthesis.

The PL-BERT project enhances text-to-speech by using a phoneme-level BERT to predict graphemes and phonemes, significantly improving speech naturalness over current models like StyleTTS. Pre-trained on a large English dataset, it is adaptable to other languages and easily integrates into various TTS models with comprehensive setup and training guides. This method efficiently generates prosodic patterns using phoneme-only inputs.