  Deep clustering (DC) and utterance-level permutation invariant training
(uPIT) have been demonstrated promising for speaker-independent speech
separation. DC is usually formulated as two-step processes: embedding learning
and embedding clustering, which results in complex separation pipelines and a
huge obstacle in directly optimizing the actual separation objectives. As for
uPIT, it only minimizes the chosen permutation with the lowest mean square
error, doesn't discriminate it with other permutations. In this paper, we
propose a discriminative learning method for speaker-independent speech
separation using deep embedding features. Firstly, a DC network is trained to
extract deep embedding features, which contain each source's information and
have an advantage in discriminating each target speakers. Then these features
are used as the input for uPIT to directly separate the different sources.
Finally, uPIT and DC are jointly trained, which directly optimizes the actual
separation objectives. Moreover, in order to maximize the distance of each
permutation, the discriminative learning is applied to fine tuning the whole
model. Our experiments are conducted on WSJ0-2mix dataset. Experimental results
show that the proposed models achieve better performances than DC and uPIT for
speaker-independent speech separation.
