# The Singing Voice Conversion Challenge 2023

arXiv: https://arxiv.org/abs/2306.14422 · HTML: https://mlsanity.com/paper/2306.14422/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 63 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 30 of 40)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://mlsanity.com/paper/2206.04658/) (2022)
- [Recent Developments on ESPnet Toolkit Boosted by Conformer](https://mlsanity.com/paper/2010.13956/) (2020)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion](https://mlsanity.com/paper/2008.12527/) (2020)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [Generalization Ability of MOS Prediction Networks](https://mlsanity.com/paper/2110.02635/) (2021)
- [The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods](https://mlsanity.com/paper/1804.04262/) (2018)
- [A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion](https://mlsanity.com/paper/2111.02392/) (2021)
- [Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus](https://mlsanity.com/paper/2112.10358/) (2021)
- [StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion](https://mlsanity.com/paper/2107.10394/) (2021)
- [Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis](https://mlsanity.com/paper/2201.07429/) (2022)
- [Pushing the limits of raw waveform speaker recognition](https://mlsanity.com/paper/2203.08488/) (2022)
- [PortaSpeech: Portable and High-Quality Generative Text-to-Speech](https://mlsanity.com/paper/2109.15166/) (2021)
- [Neural source-filter waveform models for statistical parametric speech synthesis](https://mlsanity.com/paper/1904.12088/) (2019)
- [HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis](https://mlsanity.com/paper/2009.01776/) (2020)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining](https://mlsanity.com/paper/1912.06813/) (2019)
- [Unsupervised Cross-Domain Singing Voice Conversion](https://mlsanity.com/paper/2008.02830/) (2020)
- [FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation](https://mlsanity.com/paper/2011.05731/) (2020)
- [Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders](https://mlsanity.com/paper/1912.02613/) (2019)
- [NHSS: A Speech and Singing Parallel Database](https://mlsanity.com/paper/2012.00337/) (2020)

## Built on by (most cited first, 25 of 25)

- [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)
- [BiCrossMamba-ST: Speech Deepfake Detection with Bidirectional Mamba Spectro-Temporal Cross-Attention](https://mlsanity.com/paper/2505.13930/) (2025)
- [SongBsAb: A Dual Prevention Approach against Singing Voice Conversion based Illegal Song Covers](https://mlsanity.com/paper/2401.17133/) (2024)
- [SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement](https://mlsanity.com/paper/2407.07728/) (2024)
- [Vevo2: A Unified and Controllable Framework for Speech and Singing Voice Generation](https://mlsanity.com/paper/2508.16332/) (2025)
- [From Scores to Preferences: Redefining MOS Benchmarking for Speech Quality Reward Modeling](https://mlsanity.com/paper/2510.00743/) (2025)
- [YingMusic-Singer: Zero-shot Singing Voice Synthesis and Editing with Annotation-free Melody Guidance](https://mlsanity.com/paper/2512.04779/) (2025)
- [Optimal Transport Maps are Good Voice Converters](https://mlsanity.com/paper/2411.02402/) (2024)
- [Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement](https://mlsanity.com/paper/2501.13372/) (2025)
- [Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis](https://mlsanity.com/paper/2507.02176/) (2025)
- [A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023](https://mlsanity.com/paper/2310.05203/) (2023)
- [SingVisio: Visual Analytics of Diffusion Model for Singing Voice Conversion](https://mlsanity.com/paper/2402.12660/) (2024)
- [Applications and Advances of Artificial Intelligence in Music Generation:A Review](https://mlsanity.com/paper/2409.03715/) (2024)
- [LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling](https://mlsanity.com/paper/2409.08583/) (2024)
- [DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching](https://mlsanity.com/paper/2508.05978/) (2025)
- [Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features](https://mlsanity.com/paper/2502.04722/) (2025)
- [kNN-SVC: Robust Zero-Shot Singing Voice Conversion with Additive Synthesis and Concatenation Smoothness Optimization](https://mlsanity.com/paper/2504.05686/) (2025)
- [Multi-Accent Mandarin Dry-Vocal Singing Dataset: Benchmark for Singing Accent Recognition](https://mlsanity.com/paper/2512.07005/) (2025)
- [Low-Resource Cross-Domain Singing Voice Synthesis via Reduced Self-Supervised Speech Representations](https://mlsanity.com/paper/2402.01520/) (2024)
- [Automatic design optimization of preference-based subjective evaluation with online learning in crowdsourcing environment](https://mlsanity.com/paper/2403.06100/) (2024)
- [Who is Authentic Speaker](https://mlsanity.com/paper/2405.00248/) (2024)
- [Automatic Voice Identification after Speech Resynthesis using PPG](https://mlsanity.com/paper/2408.02712/) (2024)
- [Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models](https://mlsanity.com/paper/2409.14131/) (2024)
- [Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation](https://mlsanity.com/paper/2512.06304/) (2025)
- [Controllable Singing Style Conversion with Boundary-Aware Information Bottleneck](https://mlsanity.com/paper/2604.05526/) (2026)

## Abstract (arXiv)

We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) systems based on a common dataset. This year we shifted our focus to singing voice conversion (SVC), thus named the challenge the Singing Voice Conversion Challenge (SVCC). A new database was constructed for two tasks, namely in-domain and cross-domain SVC. The challenge was run for two months, and in total we received 26 submissions, including 2 baselines. Through a large-scale crowd-sourced listening test, we observed that for both tasks, although human-level naturalness was achieved by the top system, no team was able to obtain a similarity score as high as the target speakers. Also, as expected, cross-domain SVC is harder than in-domain SVC, especially in the similarity aspect. We also investigated whether existing objective measurements were able to predict perceptual performance, and found that only few of them could reach a significant correlation.