3 citations · 6 across the 5 of their papers we have counts for
5 papers
RobustSVC: HuBERT-based Melody Extractor and Adversarial Learning for Robust Singing Voice Conversion
Wei Chen, Xintao Zhao, Jun Chen +3
Singing voice conversion (SVC) is hindered by noise sensitivity due to the use of non-robust methods for extracting pitch and energy during the inference. As clean signals are key…
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion
Xintao Zhao, Shuai Wang, Yang Chao +2
Nowadays, recognition-synthesis-based methods have been quite popular with voice conversion (VC). By introducing linguistics features with good disentangling characters extracted f…
Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
SiCheng Yang, Methawee Tantrawenith, Haolin Zhuang +8
One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic. Existing works generally disentangle timbre, while informat…
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion
Xintao Zhao, Feng Liu, Changhe Song +4
Non-parallel data voice conversion (VC) have achieved considerable breakthroughs recently through introducing bottleneck features (BNFs) extracted by the automatic speech recogniti…
Adversarially learning disentangled speech representations for robust multi-factor voice conversion
Jie Wang, Jingbei Li, Xintao Zhao +3
Factorizing speech as disentangled speech representations is vital to achieve highly controllable style transfer in voice conversion (VC). Conventional speech representation learni…