  Deep neural network with dual-path bi-directional long short-term memory
(BiLSTM) block has been proved to be very effective in sequence modeling,
especially in speech separation. This work investigates how to extend dual-path
BiLSTM to result in a new state-of-the-art approach, called TasTas, for
multi-talker monaural speech separation (a.k.a cocktail party problem). TasTas
introduces two simple but effective improvements, one is an iterative
multi-stage refinement scheme, and the other is to correct the speech with
imperfect separation through a loss of speaker identity consistency between the
separated speech and original speech, to boost the performance of dual-path
BiLSTM based networks. TasTas takes the mixed utterance of two speakers and
maps it to two separated utterances, where each utterance contains only one
speaker's voice. Our experiments on the notable benchmark WSJ0-2mix data corpus
result in 20.55dB SDR improvement, 20.35dB SI-SDR improvement, 3.69 of PESQ,
and 94.86\% of ESTOI, which shows that our proposed networks can lead to big
performance improvement on the speaker separation task. We have open sourced
our re-implementation of the DPRNN-TasNet here
(https://github.com/ShiZiqiang/dual-path-RNNs-DPRNNs-based-speech-separation),
and our TasTas is realized based on this implementation of DPRNN-TasNet, it is
believed that the results in this paper can be reproduced with ease.
