19 citations · 36 across the 5 of their papers we have counts for
5 papers
Learning Disentangled Speech Representations with Contrastive Learning and Time-Invariant Retrieval
Yimin Deng, Huaizhen Tang, Xulong Zhang +3
Voice conversion refers to transferring speaker identity with well-preserved content. Better disentanglement of speech representations leads to better voice conversion. Recent stud…
PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
Yimin Deng, Huaizhen Tang, Xulong Zhang +3
Voice conversion as the style transfer task applied to speech, refers to converting one person's speech into a new speech that sounds like another person's. Up to now, there has be…
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…
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Huaizhen Tang, Xulong Zhang, Jianzong Wang +4
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Recently, AutoVC, a conditional autoencoder based method, achieved excelle…
AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learning
Huaizhen Tang, Xulong Zhang, Jianzong Wang +2
Voice Conversion(VC) refers to changing the timbre of a speech while retaining the discourse content. Recently, many works have focused on disentangle-based learning techniques to…