# StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks

arXiv: https://arxiv.org/abs/1806.02169 · HTML: https://mlsanity.com/paper/1806.02169/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 30 of 78)

- [Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale](https://mlsanity.com/paper/2306.15687/) (2023)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era](https://mlsanity.com/paper/2210.03538/) (2022)
- [Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data](https://mlsanity.com/paper/2203.15321/) (2022)
- [Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion](https://mlsanity.com/paper/2203.16705/) (2022)
- [Towards Relatable Explainable AI with the Perceptual Process](https://mlsanity.com/paper/2112.14005/) (2021)
- [MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames](https://mlsanity.com/paper/2102.12841/) (2021)
- [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022)
- [Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units](https://mlsanity.com/paper/2212.09730/) (2022)
- ["Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World](https://mlsanity.com/paper/2109.09598/) (2021)
- [Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models](https://mlsanity.com/paper/2110.02891/) (2021)
- [Towards Improved Zero-shot Voice Conversion with Conditional DSVAE](https://mlsanity.com/paper/2205.05227/) (2022)
- [NVC-Net: End-to-End Adversarial Voice Conversion](https://mlsanity.com/paper/2106.00992/) (2021)
- [Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection](https://mlsanity.com/paper/2206.13979/) (2022)
- [SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection](https://mlsanity.com/paper/2210.06105/) (2022)
- [Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfer](https://mlsanity.com/paper/2409.06096/) (2024)
- [VocBench: A Neural Vocoder Benchmark for Speech Synthesis](https://mlsanity.com/paper/2112.03099/) (2021)
- [Face-Driven Zero-Shot Voice Conversion with Memory-based Face-Voice Alignment](https://mlsanity.com/paper/2309.09470/) (2023)
- [DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text Alignment](https://mlsanity.com/paper/2401.08095/) (2024)
- [An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation](https://mlsanity.com/paper/2107.08361/) (2021)
- [Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features](https://mlsanity.com/paper/2112.04424/) (2021)
- [Trusted Media Challenge Dataset and User Study](https://mlsanity.com/paper/2201.04788/) (2022)
- [Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations](https://mlsanity.com/paper/2402.03407/) (2024)
- [FastVoiceGrad: One-step Diffusion-Based Voice Conversion with Adversarial Conditional Diffusion Distillation](https://mlsanity.com/paper/2409.02245/) (2024)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [Global Rhythm Style Transfer Without Text Transcriptions](https://mlsanity.com/paper/2106.08519/) (2021)
- [Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning](https://mlsanity.com/paper/2110.14422/) (2021)

## Abstract (arXiv)

This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it (1) requires no parallel utterances, transcriptions, or time alignment procedures for speech generator training, (2) simultaneously learns many-to-many mappings across different attribute domains using a single generator network, (3) is able to generate converted speech signals quickly enough to allow real-time implementations and (4) requires only several minutes of training examples to generate reasonably realistic-sounding speech. Subjective evaluation experiments on a non-parallel many-to-many speaker identity conversion task revealed that the proposed method obtained higher sound quality and speaker similarity than a state-of-the-art method based on variational autoencoding GANs.