  Speech separation has been extensively studied to deal with the cocktail
party problem in recent years. All related approaches can be divided into two
categories: time-frequency domain methods and time domain methods. In addition,
some methods try to generate speaker vectors to support source separation. In
this study, we propose a new model called dual-path filter network (DPFN). Our
model focuses on the post-processing of speech separation to improve speech
separation performance. DPFN is composed of two parts: the speaker module and
the separation module. First, the speaker module infers the identities of the
speakers. Then, the separation module uses the speakers' information to extract
the voices of individual speakers from the mixture. DPFN constructed based on
DPRNN-TasNet is not only superior to DPRNN-TasNet, but also avoids the problem
of permutation-invariant training (PIT).
