28 citations · 164 across the 30 of their papers we have counts for
30 papers · 1 filter
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Meng Yu, Yong Xu, Chunlei Zhang +2
Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when…
FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Rongjie Huang, Max W. Y. Lam, Jun Wang +4
Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hinde…
Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion
Jiachen Lian, Chunlei Zhang, Dong Yu
Traditional studies on voice conversion (VC) have made progress with parallel training data and known speakers. Good voice conversion quality is obtained by exploring better alignm…
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
Max W. Y. Lam, Jun Wang, Dan Su +1
Diffusion probabilistic models (DPMs) and their extensions have emerged as competitive generative models yet confront challenges of efficient sampling. We propose a new bilateral d…
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion
Disong Wang, Shan Yang, Dan Su +3
Though significant progress has been made for speaker-dependent Video-to-Speech (VTS) synthesis, little attention is devoted to multi-speaker VTS that can map silent video to speec…
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs
Songxiang Liu, Dan Su, Dong Yu
Denoising diffusion probabilistic models (DDPMs) are expressive generative models that have been used to solve a variety of speech synthesis problems. However, because of their hig…