# The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

arXiv: https://arxiv.org/abs/1804.04262 · HTML: https://mlsanity.com/paper/1804.04262/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 30 of 68)

- [Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale](https://mlsanity.com/paper/2306.15687/) (2023)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement](https://mlsanity.com/paper/2203.13086/) (2022)
- [CDPAM: Contrastive learning for perceptual audio similarity](https://mlsanity.com/paper/2102.05109/) (2021)
- [Deep Learning Based Assessment of Synthetic Speech Naturalness](https://mlsanity.com/paper/2104.11673/) (2021)
- [The Singing Voice Conversion Challenge 2023](https://mlsanity.com/paper/2306.14422/) (2023)
- [LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech](https://mlsanity.com/paper/2110.09103/) (2021)
- [Utilizing Self-supervised Representations for MOS Prediction](https://mlsanity.com/paper/2104.03017/) (2021)
- [SpeechLMScore: Evaluating speech generation using speech language model](https://mlsanity.com/paper/2212.04559/) (2022)
- [MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames](https://mlsanity.com/paper/2102.12841/) (2021)
- [EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model](https://mlsanity.com/paper/2106.09317/) (2021)
- [SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis](https://mlsanity.com/paper/2204.03040/) (2022)
- [SpeechNet: A Universal Modularized Model for Speech Processing Tasks](https://mlsanity.com/paper/2105.03070/) (2021)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Fusion of Self-supervised Learned Models for MOS Prediction](https://mlsanity.com/paper/2204.04855/) (2022)
- [NVC-Net: End-to-End Adversarial Voice Conversion](https://mlsanity.com/paper/2106.00992/) (2021)
- [Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing](https://mlsanity.com/paper/2305.19953/) (2023)
- [Harder or Different? Understanding Generalization of Audio Deepfake Detection](https://mlsanity.com/paper/2406.03512/) (2024)
- [SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection](https://mlsanity.com/paper/2407.18517/) (2024)
- [DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores](https://mlsanity.com/paper/2204.03219/) (2022)
- [MOSPC: MOS Prediction Based on Pairwise Comparison](https://mlsanity.com/paper/2306.10493/) (2023)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection](https://mlsanity.com/paper/2304.03323/) (2023)
- [Towards General-Purpose Text-Instruction-Guided Voice Conversion](https://mlsanity.com/paper/2309.14324/) (2023)
- [RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier](https://mlsanity.com/paper/2410.22124/) (2024)
- [ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis](https://mlsanity.com/paper/2203.10473/) (2022)
- [StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contexts](https://mlsanity.com/paper/2106.00043/) (2021)
- [Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments](https://mlsanity.com/paper/2106.08873/) (2021)

## Abstract (arXiv)

We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems. The objective of the challenge was to perform speaker conversion (i.e. transform the vocal identity) of a source speaker to a target speaker while maintaining linguistic information. As an update to the previous challenge, we considered both parallel and non-parallel data to form the Hub and Spoke tasks, respectively. A total of 23 teams from around the world submitted their systems, 11 of them additionally participated in the optional Spoke task. A large-scale crowdsourced perceptual evaluation was then carried out to rate the submitted converted speech in terms of naturalness and similarity to the target speaker identity. In this paper, we present a brief summary of the state-of-the-art techniques for VC, followed by a detailed explanation of the challenge tasks and the results that were obtained.