# Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1704.00849 · HTML: https://mlsanity.com/paper/1704.00849/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 30 of 38)

- [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)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [A Large-Scale Evaluation of Speech Foundation Models](https://mlsanity.com/paper/2404.09385/) (2024)
- [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)
- [The Age of Synthetic Realities: Challenges and Opportunities](https://mlsanity.com/paper/2306.11503/) (2023)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion](https://mlsanity.com/paper/2208.08757/) (2022)
- [Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion](https://mlsanity.com/paper/2203.12813/) (2022)
- [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)
- [A Comparative Study of Self-supervised Speech Representation Based Voice Conversion](https://mlsanity.com/paper/2207.04356/) (2022)
- [A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion](https://mlsanity.com/paper/2106.01415/) (2021)
- [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)
- [NoiseVC: Towards High Quality Zero-Shot Voice Conversion](https://mlsanity.com/paper/2104.06074/) (2021)
- [SVSNet: An End-to-end Speaker Voice Similarity Assessment Model](https://mlsanity.com/paper/2107.09392/) (2021)
- [Federated Split GANs](https://mlsanity.com/paper/2207.01750/) (2022)
- [Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion](https://mlsanity.com/paper/2302.12434/) (2023)
- [Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms](https://mlsanity.com/paper/2308.09302/) (2023)
- [Conditional Deep Hierarchical Variational Autoencoder for Voice Conversion](https://mlsanity.com/paper/2112.02796/) (2021)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (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)
- [NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion](https://mlsanity.com/paper/2511.00256/) (2025)
- [StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition](https://mlsanity.com/paper/2108.04395/) (2021)
- [Training Generative Adversarial Networks with Adaptive Composite Gradient](https://mlsanity.com/paper/2111.05508/) (2021)
- [Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE](https://mlsanity.com/paper/2203.16037/) (2022)
- [A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion](https://mlsanity.com/paper/2302.13678/) (2023)

## Abstract (arXiv)

Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application scenarios. In most situations, the source and the target speakers do not repeat the same texts or they may even speak different languages. In this case, one possible, although indirect, solution is to build a generative model for speech. Generative models focus on explaining the observations with latent variables instead of learning a pairwise transformation function, thereby bypassing the requirement of speech frame alignment. In this paper, we propose a non-parallel VC framework with a variational autoencoding Wasserstein generative adversarial network (VAW-GAN) that explicitly considers a VC objective when building the speech model. Experimental results corroborate the capability of our framework for building a VC system from unaligned data, and demonstrate improved conversion quality.