1 citations · 1 across the 3 of their papers we have counts for
5 papers
PhaseGAN: High-Fidelity Vocoder via Decoupled Amplitude and GAN-Driven Phase Reconstruction
Wenzheng Zhang, Xueliang Zhang, Shulin He +7
A vocoder is a pivotal component of modern text-to-speech (TTS) systems. Despite the significant progress of neural network-based vocoders, accurate phase reconstruction remains th…
MC-LExt: Multi-Channel Target Speaker Extraction with Onset-Prompted Speaker Conditioning Mechanism
Tongtao Ling, Shulin He, Pengjie Shen +1
Multi-channel target speaker extraction (MC-TSE) aims to extract a target speaker's voice from multi-speaker signals captured by multiple microphones. Existing methods often rely o…
ARiSE: Auto-Regressive Multi-Channel Speech Enhancement
Pengjie Shen, Xueliang Zhang, Zhong-Qiu Wang
We propose ARiSE, an auto-regressive algorithm for multi-channel speech enhancement. ARiSE improves existing deep neural network (DNN) based frame-online multi-channel speech enhan…
Listen to Extract: Onset-Prompted Target Speaker Extraction
Pengjie Shen, Kangrui Chen, Shulin He +5
We propose listen to extract (LExt), a highly-effective while extremely-simple algorithm for monaural target speaker extraction (TSE). Given an enrollment utterance of a target spe…
ExARN: self-attending RNN for target speaker extraction
Pengjie Shen, Shulin He, Xueliang Zhang
Target speaker extraction is to extract the target speaker, specified by enrollment utterance, in an environment with other competing speakers. Therefore, the task needs to solve t…