The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.
CycleGAN-VC2: improved CycleGAN-based non-parallel voice conversion.
Abstract
Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been challenging due to the disadvantages of the training conditions. Recently, CycleGAN-VC has provided a breakthrough and performed comparably to a parallel VC method without relying on any extra data, modules, or time alignment procedures. However, there is still a large gap between the real target and converted speech, and bridging this gap remains a challenge. To reduce the gap, we propose CycleGAN-VC2, which is an improved version of CycleGAN-VC incorporating three new techniques: an improved objective (two-step adversarial losses), improved generator (2-1-2D CNN), and improved discriminator (PatchGAN). We evaluated our method on a non-parallel VC task and analyzed the effect of each technique in detail. An objective evaluation showed that these techniques help bring the converted feature sequence closer to the target in terms of both global and local structures, which we assess by using Mel-cepstral distortion and modulation spectra distance, respectively. A subjective evaluation showed that CycleGAN-VC2 outperforms CycleGAN-VC in terms of naturalness and similarity for every speaker pair, including intra-gender and inter-gender pairs.
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“To improve the stability of the training process, an additional cycle-consistency loss function was proposed to optimize the structure of GAN, which was defined as cycle GANs (Kaneko et al., 2019).”
From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Kaneko et al., 2019
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Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
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GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
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Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning
Şişman, B., Yamagishi, J., King, S., & Li, H. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3038524 · Cited by 399
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2236 · Cited by 170
Self-citationIntelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review
Khan, W., Zaki, N., & Ali, L. (2021) · IEEE Access · doi:10.1109/access.2021.3069937 · Cited by 133
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Metadata
- Authors
- Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N.
- Affiliations
- NTT (Japan) (JP)
- Venue
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 6820-6824
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2937579788
- Cited by
- 272
- References
- 58
- Citations by year
- 2026: 14 · 2025: 34 · 2024: 36 · 2023: 57 · 2022: 39 · 2021: 43 · 2020: 34 · 2019: 15
- FWCI
- 12.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Funders
- Virginia Agricultural Experiment Station, Virginia Polytechnic Institute and State University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- non-parallel voice conversion, adversarial training, PatchGAN discriminator, Mel cepstral distortion, speech naturalness
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