  In target speaker extraction, many studies rely on the speaker embedding
which is obtained from an enrollment of the target speaker and employed as the
guidance. However, solely using speaker embedding may not fully utilize the
contextual information contained in the enrollment. In this paper, we directly
exploit this contextual information in the time-frequency (T-F) domain.
Specifically, the T-F representations of the enrollment and the mixed signal
are interacted to compute the weighting matrices through an attention
mechanism. These weighting matrices reflect the similarity among different
frames of the T-F representations and are further employed to obtain the
consistent T-F representations of the enrollment. These consistent
representations are served as the guidance, allowing for better exploitation of
the contextual information. Furthermore, the proposed method achieves the
state-of-the-art performance on the benchmark dataset and shows its
effectiveness in the complex scenarios.
