1 citations · 1 across the 7 of their papers we have counts for
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Vec-Tok-VC+: Residual-enhanced Robust Zero-shot Voice Conversion with Progressive Constraints in a Dual-mode Training Strategy
Linhan Ma, Xinfa Zhu, Yuanjun Lv +5
Zero-shot voice conversion (VC) aims to transform source speech into arbitrary unseen target voice while keeping the linguistic content unchanged. Recent VC methods have made signi…
RaD-Net 2: A causal two-stage repairing and denoising speech enhancement network with knowledge distillation and complex axial self-attention
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
In real-time speech communication systems, speech signals are often degraded by multiple distortions. Recently, a two-stage Repair-and-Denoising network (RaD-Net) was proposed with…
RaD-Net: A Repairing and Denoising Network for Speech Signal Improvement
Mingshuai Liu, Zhuangqi Chen, Xiaopeng Yan +5
This paper introduces our repairing and denoising network (RaD-Net) for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. We extend our previous framework based on a two-s…
Vec-Tok Speech: speech vectorization and tokenization for neural speech generation
Xinfa Zhu, Yuanjun Lv, Yi Lei +5
Language models (LMs) have recently flourished in natural language processing and computer vision, generating high-fidelity texts or images in various tasks. In contrast, the curre…
SALT: Distinguishable Speaker Anonymization Through Latent Space Transformation
Yuanjun Lv, Jixun Yao, Peikun Chen +3
Speaker anonymization aims to conceal a speaker's identity without degrading speech quality and intelligibility. Most speaker anonymization systems disentangle the speaker represen…
HiGNN-TTS: Hierarchical Prosody Modeling with Graph Neural Networks for Expressive Long-form TTS
Dake Guo, Xinfa Zhu, Liumeng Xue +4
Recent advances in text-to-speech, particularly those based on Graph Neural Networks (GNNs), have significantly improved the expressiveness of short-form synthetic speech. However,…