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

• Main Authors: Xu, Chenglin, Rao, Wei, Xiao, Xiong, Chng, Eng Siong, Li, Haizhou
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Constrained Permutation Invariant Training; Grid LSTM
• Online Access: https://hdl.handle.net/10356/137336
• Institution: Nanyang Technological University

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...