# StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion

arXiv: https://arxiv.org/abs/1907.12279 · HTML: https://mlsanity.com/paper/1907.12279/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 30 of 37)

- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion](https://mlsanity.com/paper/2107.10394/) (2021)
- [SpeechSplit 2.0: Unsupervised speech disentanglement for voice conversion Without tuning autoencoder Bottlenecks](https://mlsanity.com/paper/2203.14156/) (2022)
- [Towards Relatable Explainable AI with the Perceptual Process](https://mlsanity.com/paper/2112.14005/) (2021)
- [Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models](https://mlsanity.com/paper/2206.02246/) (2022)
- [MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames](https://mlsanity.com/paper/2102.12841/) (2021)
- [StreamVC: Real-Time Low-Latency Voice Conversion](https://mlsanity.com/paper/2401.03078/) (2024)
- [Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models](https://mlsanity.com/paper/2110.02891/) (2021)
- [Hierarchical disentangled representation learning for singing voice conversion](https://mlsanity.com/paper/2101.06842/) (2021)
- [NVC-Net: End-to-End Adversarial Voice Conversion](https://mlsanity.com/paper/2106.00992/) (2021)
- [Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder](https://mlsanity.com/paper/2107.06642/) (2021)
- [FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation](https://mlsanity.com/paper/2409.02245/) (2024)
- [Global Rhythm Style Transfer Without Text Transcriptions](https://mlsanity.com/paper/2106.08519/) (2021)
- [Towards General-Purpose Text-Instruction-Guided Voice Conversion](https://mlsanity.com/paper/2309.14324/) (2023)
- [StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contexts](https://mlsanity.com/paper/2106.00043/) (2021)
- [RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding](https://mlsanity.com/paper/2506.10289/) (2025)
- [Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion](https://mlsanity.com/paper/2311.08104/) (2023)
- [Axial Residual Networks for CycleGAN-based Voice Conversion](https://mlsanity.com/paper/2102.08075/) (2021)
- [GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion](https://mlsanity.com/paper/2207.01454/) (2022)
- [Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints](https://mlsanity.com/paper/2407.03824/) (2024)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (2025)
- [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)
- [Low-Latency Real-Time Non-Parallel Voice Conversion based on Cyclic Variational Autoencoder and Multiband WaveRNN with Data-Driven Linear Prediction](https://mlsanity.com/paper/2105.09858/) (2021)
- [Music Sentiment Transfer](https://mlsanity.com/paper/2110.05765/) (2021)
- [Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE](https://mlsanity.com/paper/2203.16037/) (2022)
- [Boosting Star-GANs for Voice Conversion with Contrastive Discriminator](https://mlsanity.com/paper/2209.10088/) (2022)
- [Cross-modal Face- and Voice-style Transfer](https://mlsanity.com/paper/2302.13838/) (2023)

## Abstract (arXiv)

Non-parallel multi-domain voice conversion (VC) is a technique for learning mappings among multiple domains without relying on parallel data. This is important but challenging owing to the requirement of learning multiple mappings and the non-availability of explicit supervision. Recently, StarGAN-VC has garnered attention owing to its ability to solve this problem only using a single generator. However, there is still a gap between real and converted speech. To bridge this gap, we rethink conditional methods of StarGAN-VC, which are key components for achieving non-parallel multi-domain VC in a single model, and propose an improved variant called StarGAN-VC2. Particularly, we rethink conditional methods in two aspects: training objectives and network architectures. For the former, we propose a source-and-target conditional adversarial loss that allows all source domain data to be convertible to the target domain data. For the latter, we introduce a modulation-based conditional method that can transform the modulation of the acoustic feature in a domain-specific manner. We evaluated our methods on non-parallel multi-speaker VC. An objective evaluation demonstrates that our proposed methods improve speech quality in terms of both global and local structure measures. Furthermore, a subjective evaluation shows that StarGAN-VC2 outperforms StarGAN-VC in terms of naturalness and speaker similarity. The converted speech samples are provided at http://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/stargan-vc2/index.html.