# ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations

arXiv: https://arxiv.org/abs/2302.08137 · HTML: https://mlsanity.com/paper/2302.08137/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://mlsanity.com/paper/2006.06873/) (2020)
- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization](https://mlsanity.com/paper/1904.05742/) (2019)
- [Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations](https://mlsanity.com/paper/2110.14513/) (2021)
- [FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention](https://mlsanity.com/paper/2010.14150/) (2020)
- [S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations](https://mlsanity.com/paper/2110.06280/) (2021)
- [SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification](https://mlsanity.com/paper/2010.12653/) (2020)
- [Multi-task Voice Activated Framework using Self-supervised Learning](https://mlsanity.com/paper/2110.01077/) (2021)
- [MediumVC: Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features](https://mlsanity.com/paper/2110.02500/) (2021)

## Built on by (most cited first, 15 of 15)

- [Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance](https://mlsanity.com/paper/2502.05236/) (2025)
- [Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment](https://mlsanity.com/paper/2406.17957/) (2024)
- [SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention](https://mlsanity.com/paper/2312.08676/) (2023)
- [vec2wav 2.0: Advancing Voice Conversion via Discrete Token Vocoders](https://mlsanity.com/paper/2409.01995/) (2024)
- [SelfVC: Voice Conversion With Iterative Refinement using Self Transformations](https://mlsanity.com/paper/2310.09653/) (2023)
- [Discl-VC: Disentangled Discrete Tokens and In-Context Learning for Controllable Zero-Shot Voice Conversion](https://mlsanity.com/paper/2505.24291/) (2025)
- [ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization](https://mlsanity.com/paper/2506.01032/) (2025)
- [SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs](https://mlsanity.com/paper/2307.09435/) (2023)
- [Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching](https://mlsanity.com/paper/2411.02026/) (2024)
- [Eta-WavLM: Efficient Speaker Identity Removal in Self-Supervised Speech Representations Using a Simple Linear Equation](https://mlsanity.com/paper/2505.19273/) (2025)
- [Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling](https://mlsanity.com/paper/2410.01350/) (2024)
- [kNN-SVC: Robust Zero-Shot Singing Voice Conversion with Additive Synthesis and Concatenation Smoothness Optimization](https://mlsanity.com/paper/2504.05686/) (2025)
- [Low-Resource Cross-Domain Singing Voice Synthesis via Reduced Self-Supervised Speech Representations](https://mlsanity.com/paper/2402.01520/) (2024)
- [Beyond Waveform Robustness: Robust Feature-Vocoder Adversarial Attacks on Automatic Speech Recognition](https://mlsanity.com/paper/2606.05678/) (2026)
- [NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization](https://mlsanity.com/paper/2607.03985/) (2026)

## Abstract (arXiv)

In this work, we propose a zero-shot voice conversion method using speech representations trained with self-supervised learning. First, we develop a multi-task model to decompose a speech utterance into features such as linguistic content, speaker characteristics, and speaking style. To disentangle content and speaker representations, we propose a training strategy based on Siamese networks that encourages similarity between the content representations of the original and pitch-shifted audio. Next, we develop a synthesis model with pitch and duration predictors that can effectively reconstruct the speech signal from its decomposed representation. Our framework allows controllable and speaker-adaptive synthesis to perform zero-shot any-to-any voice conversion achieving state-of-the-art results on metrics evaluating speaker similarity, intelligibility, and naturalness. Using just 10 seconds of data for a target speaker, our framework can perform voice swapping and achieves a speaker verification EER of 5.5% for seen speakers and 8.4% for unseen speakers.