# Predictions of Subjective Ratings and Spoofing Assessments of Voice Conversion Challenge 2020 Submissions

arXiv: https://arxiv.org/abs/2009.03554 · HTML: https://mlsanity.com/paper/2009.03554/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 19 of 19)

- [StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion](https://mlsanity.com/paper/2107.10394/) (2021)
- [Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors](https://mlsanity.com/paper/2109.02993/) (2021)
- [S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations](https://mlsanity.com/paper/2110.06280/) (2021)
- [Human Perception of Audio Deepfakes](https://mlsanity.com/paper/2107.09667/) (2021)
- [A Comparative Study of Self-supervised Speech Representation Based Voice Conversion](https://mlsanity.com/paper/2207.04356/) (2022)
- [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)
- [A Benchmark for Multi-speaker Anonymization](https://mlsanity.com/paper/2407.05608/) (2024)
- [Deepfake: Definitions, Performance Metrics and Standards, Datasets and Benchmarks, and a Meta-Review](https://mlsanity.com/paper/2208.10913/) (2022)
- [Rhythm Modeling for Voice Conversion](https://mlsanity.com/paper/2307.06040/) (2023)
- [SVSNet: An End-to-end Speaker Voice Similarity Assessment Model](https://mlsanity.com/paper/2107.09392/) (2021)
- [Evaluating Methods for Ground-Truth-Free Foreign Accent Conversion](https://mlsanity.com/paper/2309.02133/) (2023)
- [On Prosody Modeling for ASR+TTS based Voice Conversion](https://mlsanity.com/paper/2107.09477/) (2021)
- [Resource-Efficient Fine-Tuning Strategies for Automatic MOS Prediction in Text-to-Speech for Low-Resource Languages](https://mlsanity.com/paper/2305.19396/) (2023)
- [LinearVC: Linear transformations of self-supervised features through the lens of voice conversion](https://mlsanity.com/paper/2506.01510/) (2025)
- [Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis](https://mlsanity.com/paper/2507.02176/) (2025)
- [Evaluation of Speech Representations for MOS prediction](https://mlsanity.com/paper/2306.09979/) (2023)
- [Objective Evaluation of Prosody and Intelligibility in Speech Synthesis via Conditional Prediction of Discrete Tokens](https://mlsanity.com/paper/2509.20485/) (2025)
- [SVSNet+: Enhancing Speaker Voice Similarity Assessment Models with Representations from Speech Foundation Models](https://mlsanity.com/paper/2406.08445/) (2024)

## Abstract (arXiv)

The Voice Conversion Challenge 2020 is the third edition under its flagship that promotes intra-lingual semiparallel and cross-lingual voice conversion (VC). While the primary evaluation of the challenge submissions was done through crowd-sourced listening tests, we also performed an objective assessment of the submitted systems. The aim of the objective assessment is to provide complementary performance analysis that may be more beneficial than the time-consuming listening tests. In this study, we examined five types of objective assessments using automatic speaker verification (ASV), neural speaker embeddings, spoofing countermeasures, predicted mean opinion scores (MOS), and automatic speech recognition (ASR). Each of these objective measures assesses the VC output along different aspects. We observed that the correlations of these objective assessments with the subjective results were high for ASV, neural speaker embedding, and ASR, which makes them more influential for predicting subjective test results. In addition, we performed spoofing assessments on the submitted systems and identified some of the VC methods showing a potentially high security risk.