NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
While recent large-scale text-to-speech (TTS) models have achieved significant progress, they still fall short in speech quality, similarity, and prosody. Considering speech intricately encompasses various attributes (e.g., content, prosody, timbre, and acoustic details) that pose significant challenges for generation, a natural idea is to factorize speech into individual subspaces representing different attributes and generate them individually. Motivated by it, we propose NaturalSpeech 3, a TTS system with novel factorized diffusion models to generate natural speech in a zero-shot way. Specifically, 1) we design a neural codec with factorized vector quantization (FVQ) to disentangle speech waveform into subspaces of content, prosody, timbre, and acoustic details; 2) we propose a factorized diffusion model to generate attributes in each subspace following its corresponding prompt. With this factorization design, NaturalSpeech 3 can effectively and efficiently model intricate speech with disentangled subspaces in a divide-and-conquer way. Experiments show that NaturalSpeech 3 outperforms the state-of-the-art TTS systems on quality, similarity, prosody, and intelligibility, and achieves on-par quality with human recordings. Furthermore, we achieve better performance by scaling to 1B parameters and 200K hours of training data.
Key facts. Cited by 222 papers in the map (data through 7 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 65 of its 76 references that are in the map. First posted on arXiv 5 Mar 2024.
Builds on
65 of 76 references.
Closest ancestors · 6
Same direction, newest first
- 2023DCTTS: Discrete Diffusion Model with Contrastive Learning for Text-to-speech Generation13 Sep
- 2023DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial Training31 Jul
- 2023StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models13 Jun
- 2023NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers18 Apr
- 2022Diffsound: Discrete Diffusion Model for Text-to-sound Generation20 Jul
- 2021Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech13 May
Other references · 59
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2017Neural Discrete Representation Learningcited by 4,650
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
- 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
- 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
- 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
- 2022MaskGIT: Masked Generative Image Transformercited by 1,082
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
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- 2021SoundStream: An End-to-End Neural Audio Codeccited by 756
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- 2022High Fidelity Neural Audio Compressioncited by 673
- 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
- 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 611
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
- 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
- 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 462
- 2022DiffuSeq: Sequence to Sequence Text Generation with Diffusion Modelscited by 439
- 2023High-Fidelity Audio Compression with Improved RVQGANcited by 423
- 2022UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022cited by 381
- 2023Voicebox: Text-Guided Multilingual Universal Speech Generation at Scalecited by 373
- 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 340
- 2023Common Diffusion Noise Schedules and Sample Steps are Flawedcited by 308
- 2021YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyonecited by 277
- 2020Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Searchcited by 269
- 2022BigVGAN: A Universal Neural Vocoder with Large-Scale Trainingcited by 245
- 2021Speech Resynthesis from Discrete Disentangled Self-Supervised Representationscited by 243
- 2018Streaming End-to-end Speech Recognition For Mobile Devicescited by 230
- 2017Parallel WaveNet: Fast High-Fidelity Speech Synthesiscited by 214
- 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
- 2023Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervisioncited by 167
- 2021DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanismcited by 154
- 2022NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Qualitycited by 151
- 2023UniAudio: An Audio Foundation Model Toward Universal Audio Generationcited by 127
- 2023SoundStorm: Efficient Parallel Audio Generationcited by 111
- 2021AdaSpeech: Adaptive Text to Speech for Custom Voicecited by 98
- 2023HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codeccited by 95
- 2023InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Promptcited by 90
- 2020Unsupervised Speech Decomposition via Triple Information Bottleneckcited by 87
- 2023SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Modelscited by 85
- 2021Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representationscited by 81
- 2017Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learningcited by 67
- 2022Improved Masked Image Generation with Token-Criticcited by 63
- 2023Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Biascited by 51
- 2020Parallel Tacotron: Non-Autoregressive and Controllable TTScited by 49
- 2022NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesiscited by 38
50 most cited of 59 All 59 (JSON)
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Citing papers per year: 2024: 68; 2025: 80; 2026: 74.
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- 2026Modeling, Scaling, and Decoding: Optimizing Controllable Speech Generation with Nonverbal Vocalizations13 Sep 2026
- 2026Continuous-Time Acoustic Modelling with Neural Controlled Differential Equations10 Sep 2026
- 2026HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence27 Aug 2026
- 2026A survey of AI-generated voices and their detection15 Aug 2026
- 2026Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder12 Aug 2026
- 2026Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens31 Jul 2026
- 2026Teffic-Audio: Tell Fact from Fiction30 Jul 2026
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