In recent years, text-to-speech (TTS) synthesis technology has faced numerous challenges, particularly in achieving high-quality speech output. Due to the multiple complex attributes involved in speech, such as content, prosody, timbre, and acoustic details, achieving zero-shot TTS while maintaining sound quality, similarity, and rhythmic harmony has been a hot and difficult research topic in the industry. Microsoft Research Asia, in collaboration with renowned universities both domestically and internationally, has successfully developed NaturalSpeech 3, an advanced TTS system. This system utilizes a unique decomposition diffusion model to generate high-quality speech through zero-shot learning, breaking through the limitations of traditional TTS technology. The core of NaturalSpeech 3 lies in decomposing speech waveforms into independent subspaces of content, prosody, timbre, and acoustic details, and generating corresponding attributes in each subspace through the decomposition diffusion model. This decomposition method simplifies the complexity of speech, improving learning efficiency and the accuracy of attribute control. The latest advancements in TTS research are mainly reflected in four key areas: zero-shot synthesis, speech representation, generation methods, and attribute separation. The goal of zero-shot TTS technology is to generate high-quality speech for unseen speakers using advanced data representation and modeling techniques. In terms of speech representation, researchers have gradually transitioned from traditional waveform and mel-spectrogram methods to more data-driven methods, such as discrete tokens and continuous vectors. In terms of generation methods, both autoregressive (AR) and non-autoregressive (NAR) models have their own advantages, with NAR models demonstrating excellent robustness and speed, while AR models excel in diversity and expressiveness. As for attribute separation techniques, they aim to effectively separate speech attributes such as content, prosody, and timbre through tools like neural speech codecs, enhancing the overall quality of synthesized speech. The prominent advantages of NaturalSpeech 3 lie in its high quality, similarity, and controllability. It utilizes advanced neural speech codecs (FACodec) and the decomposition diffusion model to finely process the various attributes of speech. This innovative approach not only ensures the quality and controllability of synthesized speech but also enables more diversified application scenarios compared to previous versions. Through extensive evaluations on large datasets such as LibriSpeech and RAVDESS, NaturalSpeech 3 has made significant progress in terms of generation quality, speaker similarity, and prosodic similarity. Furthermore, the scalability of the system has been fully validated, with performance further improved through the use of larger datasets and model sizes. However, it is worth noting that NaturalSpeech 3 currently relies mainly on English data from LibriVox, which limits its diversity of voices and multilingual capabilities. To overcome this limitation, researchers are planning to expand the scope of data collection to cover more languages and voice types. In conclusion, NaturalSpeech 3 has brought revolutionary breakthroughs to the field of TTS synthesis with its unique decomposition diffusion model and advanced neural speech coding technology. With continuous technological advancements and expanding datasets, we have reason to believe that future TTS systems will provide us with more natural and realistic speech experiences.