7 citations · 8 across the 6 of their papers we have counts for
6 papers
Noise-robust voice conversion with domain adversarial training
Hongqiang Du, Lei Xie, Haizhou Li
Voice conversion has made great progress in the past few years under the studio-quality test scenario in terms of speech quality and speaker similarity. However, in real applicatio…
IQDUBBING: Prosody modeling based on discrete self-supervised speech representation for expressive voice conversion
Wendong Gan, Bolong Wen, Ying Yan +6
Prosody modeling is important, but still challenging in expressive voice conversion. As prosody is difficult to model, and other factors, e.g., speaker, environment and content, wh…
One-shot Voice Conversion For Style Transfer Based On Speaker Adaptation
Zhichao Wang, Qicong Xie, Tao Li +4
One-shot style transfer is a challenging task, since training on one utterance makes model extremely easy to over-fit to training data and causes low speaker similarity and lack of…
Enriching Source Style Transfer in Recognition-Synthesis based Non-Parallel Voice Conversion
Zhichao Wang, Xinyong Zhou, Fengyu Yang +6
Current voice conversion (VC) methods can successfully convert timbre of the audio. As modeling source audio's prosody effectively is a challenging task, there are still limitation…
Improving robustness of one-shot voice conversion with deep discriminative speaker encoder
Hongqiang Du, Lei Xie
One-shot voice conversion has received significant attention since only one utterance from source speaker and target speaker respectively is required. Moreover, source speaker and…
Optimizing voice conversion network with cycle consistency loss of speaker identity
Hongqiang Du, Xiaohai Tian, Lei Xie +1
We propose a novel training scheme to optimize voice conversion network with a speaker identity loss function. The training scheme not only minimizes frame-level spectral loss, but…