# Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion

arXiv: https://arxiv.org/abs/2311.08104 · HTML: https://mlsanity.com/paper/2311.08104/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 100 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 30 of 50)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Efficient Neural Audio Synthesis](https://mlsanity.com/paper/1802.08435/) (2018)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (2018)
- [Unsupervised Speech Decomposition via Triple Information Bottleneck](https://mlsanity.com/paper/2004.11284/) (2020)
- [Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations](https://mlsanity.com/paper/2110.14513/) (2021)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion](https://mlsanity.com/paper/1904.04631/) (2019)
- [NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesis](https://mlsanity.com/paper/2211.09407/) (2022)
- [StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion](https://mlsanity.com/paper/1907.12279/) (2019)
- [F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder](https://mlsanity.com/paper/2004.07370/) (2020)
- [VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture](https://mlsanity.com/paper/2006.04154/) (2020)
- [AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learning](https://mlsanity.com/paper/2202.10020/) (2022)
- [Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations](https://mlsanity.com/paper/1906.10508/) (2019)
- [Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations](https://mlsanity.com/paper/2010.12231/) (2020)
- [LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Models](https://mlsanity.com/paper/2306.10521/) (2023)
- [Adversarially learning disentangled speech representations for robust multi-factor voice conversion](https://mlsanity.com/paper/2102.00184/) (2021)
- [Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion](https://mlsanity.com/paper/2208.08757/) (2022)
- [Unsupervised Representation Disentanglement using Cross Domain Features and Adversarial Learning in Variational Autoencoder based Voice Conversion](https://mlsanity.com/paper/2001.07849/) (2020)
- [Disentanglement of Emotional Style and Speaker Identity for Expressive Voice Conversion](https://mlsanity.com/paper/2110.10326/) (2021)
- [Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques](https://mlsanity.com/paper/2104.00931/) (2021)
- [GAZEV: GAN-Based Zero-Shot Voice Conversion over Non-parallel Speech Corpus](https://mlsanity.com/paper/2010.12788/) (2020)

## Built on by (most cited first, 3 of 3)

- [Audio-based Kinship Verification Using Age Domain Conversion](https://mlsanity.com/paper/2410.11120/) (2024)
- [StarVC: A Unified Auto-Regressive Framework for Joint Text and Speech Generation in Voice Conversion](https://mlsanity.com/paper/2506.02414/) (2025)
- [MF-Speech: Achieving Fine-Grained and Compositional Control in Speech Generation via Factor Disentanglement](https://mlsanity.com/paper/2511.12074/) (2025)

## Abstract (arXiv)

Research on deep learning-powered voice conversion (VC) in speech-to-speech scenarios is getting increasingly popular. Although many of the works in the field of voice conversion share a common global pipeline, there is a considerable diversity in the underlying structures, methods, and neural sub-blocks used across research efforts. Thus, obtaining a comprehensive understanding of the reasons behind the choice of the different methods in the voice conversion pipeline can be challenging, and the actual hurdles in the proposed solutions are often unclear. To shed light on these aspects, this paper presents a scoping review that explores the use of deep learning in speech analysis, synthesis, and disentangled speech representation learning within modern voice conversion systems. We screened 621 publications from more than 38 different venues between the years 2017 and 2023, followed by an in-depth review of a final database consisting of 123 eligible studies. Based on the review, we summarise the most frequently used approaches to voice conversion based on deep learning and highlight common pitfalls within the community. Lastly, we condense the knowledge gathered, identify main challenges and provide recommendations for future research directions.