  Speech separation has been extensively explored to tackle the cocktail party
problem. However, these studies are still far from having enough generalization
capabilities for real scenarios. In this work, we raise a common strategy named
Speaker-Conditional Chain Model to process complex speech recordings. In the
proposed method, our model first infers the identities of variable numbers of
speakers from the observation based on a sequence-to-sequence model. Then, it
takes the information from the inferred speakers as conditions to extract their
speech sources. With the predicted speaker information from whole observation,
our model is helpful to solve the problem of conventional speech separation and
speaker extraction for multi-round long recordings. The experiments from
standard fully-overlapped speech separation benchmarks show comparable results
with prior studies, while our proposed model gets better adaptability for
multi-round long recordings.
