# How Far Are We from Robust Voice Conversion: A Survey

arXiv: https://arxiv.org/abs/2011.12063 · HTML: https://mlsanity.com/paper/2011.12063/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

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

- [Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation](https://mlsanity.com/paper/2407.05361/) (2024)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models](https://mlsanity.com/paper/2401.17230/) (2024)
- [Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models](https://mlsanity.com/paper/2509.02859/) (2025)
- [Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation](https://mlsanity.com/paper/2501.15907/) (2025)
- [Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features](https://mlsanity.com/paper/2112.04424/) (2021)
- [Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR](https://mlsanity.com/paper/2603.06164/) (2026)
- [Toward Degradation-Robust Voice Conversion](https://mlsanity.com/paper/2110.07537/) (2021)
- [Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer](https://mlsanity.com/paper/2310.17049/) (2023)
- [Low-latency Real-time Voice Conversion on CPU](https://mlsanity.com/paper/2311.00873/) (2023)
- [Fast-VGAN: Lightweight Voice Conversion with Explicit Control of F0 and Duration Parameters](https://mlsanity.com/paper/2507.04817/) (2025)
- [Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning](https://mlsanity.com/paper/2411.19770/) (2024)
- [RoVo: Robust Voice Protection Against Unauthorized Speech Synthesis with Embedding-Level Perturbations](https://mlsanity.com/paper/2505.12686/) (2025)
- [Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation](https://mlsanity.com/paper/2512.06304/) (2025)

## Abstract (arXiv)

Voice conversion technologies have been greatly improved in recent years with the help of deep learning, but their capabilities of producing natural sounding utterances in different conditions remain unclear. In this paper, we gave a thorough study of the robustness of known VC models. We also modified these models, such as the replacement of speaker embeddings, to further improve their performances. We found that the sampling rate and audio duration greatly influence voice conversion. All the VC models suffer from unseen data, but AdaIN-VC is relatively more robust. Also, the speaker embedding jointly trained is more suitable for voice conversion than those trained on speaker identification.