# VoiceGrad: Non-Parallel Any-to-Many Voice Conversion with Annealed Langevin Dynamics

arXiv: https://arxiv.org/abs/2010.02977 · HTML: https://mlsanity.com/paper/2010.02977/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme](https://mlsanity.com/paper/2109.13821/) (2021)
- [Multi-instrument Music Synthesis with Spectrogram Diffusion](https://mlsanity.com/paper/2206.05408/) (2022)
- [Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models](https://mlsanity.com/paper/2206.02246/) (2022)
- [FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation](https://mlsanity.com/paper/2409.02245/) (2024)
- [Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives](https://mlsanity.com/paper/2112.13339/) (2021)
- [VoicePrompter: Robust Zero-Shot Voice Conversion with Voice Prompt and Conditional Flow Matching](https://mlsanity.com/paper/2501.17612/) (2025)
- [Generative Probabilistic Image Colorization](https://mlsanity.com/paper/2109.14518/) (2021)
- [FasterVoiceGrad: Faster One-step Diffusion-Based Voice Conversion with Adversarial Diffusion Conversion Distillation](https://mlsanity.com/paper/2508.17868/) (2025)
- [Vocoder-Projected Feature Discriminator](https://mlsanity.com/paper/2508.17874/) (2025)
- [LatentVoiceGrad: Nonparallel Voice Conversion with Latent Diffusion/Flow-Matching Models](https://mlsanity.com/paper/2509.08379/) (2025)
- [MeanVoiceFlow: One-step Nonparallel Voice Conversion with Mean Flows](https://mlsanity.com/paper/2602.18104/) (2026)
- [DisC-VC: Disentangled and F0-Controllable Neural Voice Conversion](https://mlsanity.com/paper/2210.11059/) (2022)
- [CrossSpeech++: Cross-lingual Speech Synthesis with Decoupled Language and Speaker Generation](https://mlsanity.com/paper/2412.20048/) (2024)
- [MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion](https://mlsanity.com/paper/2609.40087/) (2026)

## Abstract (arXiv)

In this paper, we propose a non-parallel any-to-many voice conversion (VC) method termed VoiceGrad. Inspired by WaveGrad, a recently introduced novel waveform generation method, VoiceGrad is based upon the concepts of score matching and Langevin dynamics. It uses weighted denoising score matching to train a score approximator, a fully convolutional network with a U-Net structure designed to predict the gradient of the log density of the speech feature sequences of multiple speakers, and performs VC by using annealed Langevin dynamics to iteratively update an input feature sequence towards the nearest stationary point of the target distribution based on the trained score approximator network. Thanks to the nature of this concept, VoiceGrad enables any-to-many VC, a VC scenario in which the speaker of input speech can be arbitrary, and allows for non-parallel training, which requires no parallel utterances or transcriptions.