Voice Conversion Challenge 2020 –- Intra-lingual semi-parallel and cross-lingual voice conversion –-
Open accessAbstract
The voice conversion challenge is a bi-annual scientific event held to compare and understand different voice conversion (VC) systems built on a common dataset.In 2020, we organized the third edition of the challenge and constructed and distributed a new database for two tasks, intra-lingual semiparallel and cross-lingual VC.After a two-month challenge period, we received 33 submissions, including 3 baselines built on the database.From the results of crowd-sourced listening tests, we observed that VC methods have progressed rapidly thanks to advanced deep learning methods.In particular, speaker similarity scores of several systems turned out to be as high as target speakers in the intra-lingual semi-parallel VC task.However, we confirmed that none of them have achieved human-level naturalness yet for the same task.The cross-lingual conversion task is, as expected, a more difficult task, and the overall naturalness and similarity scores were lower than those for the intra-lingual conversion task.However, we observed encouraging results, and the MOS scores of the best systems were higher than 4.0.We also show a few additional analysis results to aid in understanding cross-lingual VC better.
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The VCC 2018 and VCC 2020 databases are publicly available but neither has been used previously for ASVspoof challenges.
“Both are publicly available but neither has been used previously for ASVspoof challenges.”
From ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2023) · cites this paper as [32]
The Introduction describes VCC as using a freely available dataset, Section 2.2 describes the VCC 2018 databases, and Section 3.4 says the VCC 2020 training databases were released. These passages support the availability claim in broad terms, but do not establish public availability of both specific databases. No passage found addresses whether either database had previously been used for an ASVspoof challenge. (LLM, conf=0.78)
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Style Equalization: Unsupervised Learning of Controllable Generative Sequence ModelsPreprint
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On Prosody Modeling for ASR+TTS Based Voice Conversion
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Self-citationWang, R., Du, J., & Gao, T. H. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096141 · Cited by 11 · see citing works
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SVSNet: An End-to-End Speaker Voice Similarity Assessment Model
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Self-citationAC-VC: Non-Parallel Low Latency Phonetic Posteriorgrams Based Voice Conversion
Ronssin, D., & Cerňak, M. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688277 · Cited by 9 · see citing works
DGC-Vector: A New Speaker Embedding for Zero-Shot Voice Conversion
Xiao, R., Zhang, H., & Lin, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746278 · Cited by 9 · see citing works
Benchmarking and challenges in security and privacy for voice biometrics
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Self-citationSudro, P. N., Das, R. K., Sinha, R., & Prasanna, S. R. M. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383543 · Cited by 8 · see citing works
Self-citationGu, Y., Zhang, Z., Yi, X., & Zhao, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02500 · Cited by 8 · see citing works
Improving Severity Preservation of Healthy-to-Pathological Voice Conversion With Global Style Tokens
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Self-citationSynthetic Speech Detection Based on the Temporal Consistency of Speaker Features
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StarGAN-ZSVC: Towards Zero-Shot Voice Conversion in Low-Resource Contexts
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Bargum, A. R., Serafin, S., & Erkut, C. (2024) · Frontiers in Signal Processing · doi:10.3389/frsip.2024.1339159 · Cited by 9 · see citing works
RawTFNet: A Lightweight CNN Architecture for Speech Anti-Spoofing
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Self-citationEstimating the Confidence of Speech Spoofing Countermeasure
Wang, X., & Yamagishi, J. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746204 · Cited by 6 · see citing works
Self-citationNoisy-to-Noisy Voice Conversion Under Variations of Noisy Condition
Xie, C., & Toda, T. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3313426 · Cited by 5 · see citing works
Self-citationListening to Sounds of Silence for Audio replay attack detection
Hajipour, M., Akhaee, M. A., & Toosi, R. (2021) · International Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis54653.2021.9729353 · Cited by 5 · see citing works
Voice Conversion Based on Deep Neural Networks for Time-Variant Linear Transformations
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Towards General-Purpose Text-Instruction-Guided Voice Conversion
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Self-citationAn Evaluation of Three-Stage Voice Conversion Framework for Noisy and Reverberant Conditions
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Self-citationTransfer Learning from Monolingual ASR to Transcription-free Cross-lingual Voice ConversionPreprint
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VC-AUG: Voice Conversion Based Data Augmentation for Text-Dependent Speaker Verification
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Anonymizing Speaker Voices: Easy to Imitate, Difficult to Recognize?
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Ke, D., Yao, W., Hu, R., Huang, L., Luo, Q., & Shu, W. (2022) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd54884.2022.10051811 · Cited by 3 · see citing works
The Academia Sinica Systems of Voice Conversion for VCC2020
Peng, Y., Hu, C.-H., Kang, A., Lee, H.-S., Chen, P., Tsao, Y., & Wang, H. (2020) · doi:10.21437/vcc_bc.2020-28 · Cited by 3 · see citing works
Wang, X., Delgado, H., Evans, N., Liu, X., Kinnunen, T., Tak, H., Lee, K. A., Kukanov, I., Sahidullah, M., Todisco, M., et al. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3682962 · Cited by 4 · see citing works
Self-citationSpeak Like a Dog: Human to Non-human creature Voice Conversion
Suzuki, K., Sakamoto, S., Taniguchi, T., & Kameoka, H. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9980306 · Cited by 2 · see citing works
Unsupervised Voice Type Discrimination Score Adaptation Using X-Vector Clusters
Lindsey, M., Vuong, T., & Stern, R. M. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097194 · Cited by 2 · see citing works
Progressive Residual Extraction Based Pre-Training for Speech Representation Learning
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Self-citationLatent linguistic embedding for cross-lingual text-to-speech and voice conversion
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Self-citationWell Said: An Analysis of the Speech Characteristics in the LibriSpeech Corpus
Debnath, D., Martinez, H. B., & Hines, A. (2023) · Irish Signals and Systems Conference · doi:10.1109/issc59246.2023.10162078 · Cited by 2 · see citing works
Pathological voice adaptation with autoencoder-based voice conversion
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Self-citationYang, Y., Zhang, H., Qin, X., Liang, S., Cui, H., Xu, M., & Li, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10832 · Cited by 2 · see citing works
FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning
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Speech Synthesis along Perceptual Voice Quality Dimensions
Rautenberg, F., Kuhlmann, M. T., Seebauer, F., Wiechmann, J., Wagner, P., & Haeb‐Umbach, R. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888012 · Cited by 1 · see citing works
MARS6: A Small and Robust Hierarchical-Codec Text-to-Speech Model
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IAFF-VC: Any-to-Any Voice Conversion Using Attentional Feature Fusion
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A Study on Low-Latency Recognition-Synthesis-Based Any-to-One Voice Conversion
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Self-citation
- Authors
- Yi, Z., Huang, W.-C., Tian, X., Yamagishi, J., Das, R. K., Kinnunen, T., Ling, Z.-H., & Toda, T.
- Affiliations
- National Institute of Informatics (JP)
- Nagoya University (JP)
- National University of Singapore (SG)
- University of Eastern Finland (FI)
- University of Science and Technology of China (CN)
- ORCID
- Venue
- Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020
- Publisher
- ISCA
- Published
- 2020-10-30
- Pages
- 80-98
- OpenAlex
- W3082130377
- Cited by
- 126
- References
- 54
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 9 · 2023: 27 · 2022: 26 · 2021: 33 · 2020: 8
- FWCI
- 8.85
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Funders
- National Research Foundation
- Ministry of Education, Culture, Sports, Science and Technology (18H04112, 17H04687, 16H06302, 18H04120, 18KT0051, CREST)
- National Natural Science Foundation of China (JPMJCR19A3, 61871358)
- Japan Society for the Promotion of Science (17H06101, 19K24373, JPMJCR18A6, 18H04120, 18H04112, CREST, 16H06302, 17H04687, 18KT0051)
- Core Research for Evolutional Science and Technology (JP-MJCR19A3, JPMJCR18A6)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Keywords
- voice conversion, cross-lingual voice conversion, listening tests, speaker similarity, naturalness, deep learning
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