activity
20172023
most citedUncovering Latent Style Factors for Expressive Speech Synthesis

44 citations · 155 across the 20 of their papers we have counts for

collaborators

29 papers

eess.AS2021

Cloning one's voice using very limited data in the wild

Dongyang Dai, Yuanzhe Chen, Li Chen +6

With the increasing popularity of speech synthesis products, the industry has put forward more requirements for personalized speech synthesis: (1) How to use low-resource, easily a…

cs.SD202125 cited

VoiceFixer: Toward General Speech Restoration with Neural Vocoder

Haohe Liu, Qiuqiang Kong, Qiao Tian +4

Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on single-task speech restoration (SSR), such as speech denoising or speech declipping.…

cs.SD202138 cited

Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation

Qiuqiang Kong, Yin Cao, Haohe Liu +2

Deep neural network based methods have been successfully applied to music source separation. They typically learn a mapping from a mixture spectrogram to a set of source spectrogra…

cs.SD20219 cited

The ByteDance Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2021

Keke Wang, Xudong Mao, Hao Wu +4

This paper describes the ByteDance speaker diarization system for the fourth track of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). The VoxSRC-21 provides both the d…

cs.SD20219 cited

Joint Echo Cancellation and Noise Suppression based on Cascaded Magnitude and Complex Mask Estimation

Xiaofeng Shu, Yehang Zhu, Yanjie Chen +4

Acoustic echo and background noise can seriously degrade the intelligibility of speech. In practice, echo and noise suppression are usually treated as two separated tasks and can b…

cs.SD2021

Audiovisual Singing Voice Separation

Bochen Li, Yuxuan Wang, Zhiyao Duan

Separating a song into vocal and accompaniment components is an active research topic, and recent years witnessed an increased performance from supervised training using deep learn…