10 citations · 14 across the 13 of their papers we have counts for
9 papers · 1 filter
REF-VC: Robust, Expressive and Fast Zero-Shot Voice Conversion with Diffusion Transformers
Yuepeng Jiang, Ziqian Ning, Shuai Wang +5
In real-world voice conversion applications, environmental noise in source speech and user demands for expressive output pose critical challenges. Traditional ASR-based methods ens…
E1 TTS: Simple and Fast Non-Autoregressive TTS
Zhijun Liu, Shuai Wang, Pengcheng Zhu +2
This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distributi…
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion
Ziqian Ning, Shuai Wang, Pengcheng Zhu +4
Streaming voice conversion has become increasingly popular for its potential in real-time applications. The recently proposed DualVC 2 has achieved robust and high-quality streamin…
DualVC 2: Dynamic Masked Convolution for Unified Streaming and Non-Streaming Voice Conversion
Ziqian Ning, Yuepeng Jiang, Pengcheng Zhu +4
Voice conversion is becoming increasingly popular, and a growing number of application scenarios require models with streaming inference capabilities. The recently proposed DualVC…
Multi-GradSpeech: Towards Diffusion-based Multi-Speaker Text-to-speech Using Consistent Diffusion Models
Heyang Xue, Shuai Guo, Pengcheng Zhu +1
Despite imperfect score-matching causing drift in training and sampling distributions of diffusion models, recent advances in diffusion-based acoustic models have revolutionized da…
DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding
Ziqian Ning, Yuepeng Jiang, Pengcheng Zhu +4
Voice conversion is an increasingly popular technology, and the growing number of real-time applications requires models with streaming conversion capabilities. Unlike typical (non…
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
Ziqian Ning, Qicong Xie, Pengcheng Zhu +5
Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To addr…
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…
VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis
Yongmao Zhang, Jian Cong, Heyang Xue +3
In this paper, we propose VISinger, a complete end-to-end high-quality singing voice synthesis (SVS) system that directly generates audio waveform from lyrics and musical score. Ou…