# Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks

arXiv: https://arxiv.org/abs/1711.11293 · HTML: https://mlsanity.com/paper/1711.11293/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 30 of 31)

- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [SafeEar: Content Privacy-Preserving Audio Deepfake Detection](https://mlsanity.com/paper/2409.09272/) (2024)
- [Dance Style Transfer with Cross-modal Transformer](https://mlsanity.com/paper/2208.09406/) (2022)
- [MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames](https://mlsanity.com/paper/2102.12841/) (2021)
- [Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training](https://mlsanity.com/paper/2103.16809/) (2021)
- [Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units](https://mlsanity.com/paper/2212.09730/) (2022)
- [NVC-Net: End-to-End Adversarial Voice Conversion](https://mlsanity.com/paper/2106.00992/) (2021)
- [Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks](https://mlsanity.com/paper/2112.06327/) (2021)
- [CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection](https://mlsanity.com/paper/2202.07831/) (2022)
- [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)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [Towards General-Purpose Text-Instruction-Guided Voice Conversion](https://mlsanity.com/paper/2309.14324/) (2023)
- [DarkGAN: Exploiting Knowledge Distillation for Comprehensible Audio Synthesis with GANs](https://mlsanity.com/paper/2108.01216/) (2021)
- [Supervising 3D Talking Head Avatars with Analysis-by-Audio-Synthesis](https://mlsanity.com/paper/2504.13386/) (2025)
- [NoiseVC: Towards High Quality Zero-Shot Voice Conversion](https://mlsanity.com/paper/2104.06074/) (2021)
- [Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networks](https://mlsanity.com/paper/2110.01164/) (2021)
- [Speak Like a Dog: Human to Non-human creature Voice Conversion](https://mlsanity.com/paper/2206.04780/) (2022)
- [Axial Residual Networks for CycleGAN-based Voice Conversion](https://mlsanity.com/paper/2102.08075/) (2021)
- [Fast-VGAN: Lightweight Voice Conversion with Explicit Control of F0 and Duration Parameters](https://mlsanity.com/paper/2507.04817/) (2025)
- [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)
- [Cross-modal Face- and Voice-style Transfer](https://mlsanity.com/paper/2302.13838/) (2023)
- [MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning](https://mlsanity.com/paper/2306.12785/) (2023)
- [Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion](https://mlsanity.com/paper/2504.13791/) (2025)
- [Voice Privacy with Smart Digital Assistants in Educational Settings](https://mlsanity.com/paper/2104.11038/) (2021)
- [A Deep-Bayesian Framework for Adaptive Speech Duration Modification](https://mlsanity.com/paper/2107.04973/) (2021)
- [Beyond Voice Identity Conversion: Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations](https://mlsanity.com/paper/2107.12346/) (2021)
- [CycleFlow: Purify Information Factors by Cycle Loss](https://mlsanity.com/paper/2110.09928/) (2021)

## Abstract (arXiv)

We propose a parallel-data-free voice-conversion (VC) method that can learn a mapping from source to target speech without relying on parallel data. The proposed method is general purpose, high quality, and parallel-data free and works without any extra data, modules, or alignment procedure. It also avoids over-smoothing, which occurs in many conventional statistical model-based VC methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial network (CycleGAN) with gated convolutional neural networks (CNNs) and an identity-mapping loss. A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses. This makes it possible to find an optimal pseudo pair from unpaired data. Furthermore, the adversarial loss contributes to reducing over-smoothing of the converted feature sequence. We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss. This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information. We evaluated our method on a parallel-data-free VC task. An objective evaluation showed that the converted feature sequence was near natural in terms of global variance and modulation spectra. A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture model-based method under advantageous conditions with parallel and twice the amount of data.