1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.SD2022★ 1 cited
Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation
Xiaoyu Liu, Xu Li, Joan Serrà
Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep…
cs.SD2021
On permutation invariant training for speech source separation
Xiaoyu Liu, Jordi Pons
We study permutation invariant training (PIT), which targets at the permutation ambiguity problem for speaker independent source separation models. We extend two state-of-the-art P…
eess.AS2020
An empirical study of Conv-TasNet
Berkan Kadioglu, Michael Horgan, Xiaoyu Liu +3
Conv-TasNet is a recently proposed waveform-based deep neural network that achieves state-of-the-art performance in speech source separation. Its architecture consists of a learnab…