Single channel speech separation with constrained utterance level permutation invariant training using grid LSTM

Single channel speech separation with constrained utterance level permutation invariant training using grid LSTM

Utterance level permutation invariant training (uPIT) technique is a state-of-the-art deep learning architecture for speaker independent multi-talker separation. uPIT solves the label ambiguity problem by minimizing the mean square error (MSE) over all permutations between outputs and targets. Howev...

Full description

Bibliographic Details
Main Authors: Xu, Chenglin, Rao, Wei, Xiao, Xiong, Chng, Eng Siong, Li, Haizhou
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2020
Subjects:
Engineering::Computer science and engineering
Constrained Permutation Invariant Training
Grid LSTM
Online Access:https://hdl.handle.net/10356/137336

Internet

https://hdl.handle.net/10356/137336

Similar Items