Recent advancements in text-to-speech (TTS) synthesis have faced challenges in creating high-quality speech due to its complexity, which involves content, prosody, timbre, and acoustic details. While increasing dataset size and model complexity has shown promise for zero-shot TTS, issues with voice quality, similarity, and prosody continue.
### Introducing NaturalSpeech 3 System
A team of researchers from Microsoft Research Asia, Microsoft Azure Speech, and various universities have developed a TTS system called NaturalSpeech 3. This system uses factorized diffusion models to generate top-notch speech in a zero-shot manner. By employing a neural codec with factorized vector quantization (FVQ), the system disentangles speech into different subspaces like content, prosody, timbre, and acoustic details. This simplifies speech representation for better learning and control.
### Recent Advances in TTS Research
Recent progress in TTS research focuses on zero-shot synthesis, speech representations, generation methods, and attribute disentanglement. Zero-shot TTS aims to create speech for new speakers using different data representations and techniques. Speech representations have evolved, from traditional methods to data-driven approaches. Generation methods vary between autoregressive and non-autoregressive models. Attribute disentanglement aims to separate speech attributes for improved synthesis quality.
### Advantages of NaturalSpeech 3 System
NaturalSpeech 3 is a top-of-the-line TTS system that emphasizes high quality, similarity, and control. It uses a neural speech codec (FACodec) and a factorized diffusion model to handle speech attributes effectively. This leads to superior synthesis quality and control. By leveraging large datasets for zero-shot synthesis, the system ensures efficient and effective speech synthesis with enhanced quality and controllability.
In conclusion, NaturalSpeech 3 demonstrates significant advancements in speech quality, similarity, prosody, and intelligibility. It disentangles speech attributes and synthesizes them with discrete diffusion. By scaling the model and data, the system continues to enhance its performance. Challenges related to voice diversity and multilingual capabilities are areas of future research for the team. For more information, you can check out the Paper for this research. All credit for this research goes to the researchers of this project.