  Leveraging additional speaker information to facilitate speech separation has
received increasing attention in recent years. Recent research includes
extracting target speech by using the target speaker's voice snippet and
jointly separating all participating speakers by using a pool of additional
speaker signals, which is known as speech separation using speaker inventory
(SSUSI). However, all these systems ideally assume that the pre-enrolled
speaker signals are available and are only evaluated on simple data
configurations. In realistic multi-talker conversations, the speech signal
contains a large proportion of non-overlapped regions, where we can derive
robust speaker embedding of individual talkers. In this work, we adopt the
SSUSI model in long recordings and propose a self-informed, clustering-based
inventory forming scheme for long recording, where the speaker inventory is
fully built from the input signal without the need for external speaker
signals. Experiment results on simulated noisy reverberant long recording
datasets show that the proposed method can significantly improve the separation
performance across various conditions.
