  Target speaker extraction aims to extract the speech of a specific speaker
from a multi-talker mixture as specified by an auxiliary reference. Most
studies focus on the scenario where the target speech is highly overlapped with
the interfering speech. However, this scenario only accounts for a small
percentage of real-world conversations. In this paper, we aim at the sparsely
overlapped scenarios in which the auxiliary reference needs to perform two
tasks simultaneously: detect the activity of the target speaker and disentangle
the active speech from any interfering speech. We propose an audio-visual
speaker extraction model named ActiveExtract, which leverages speaking activity
from audio-visual active speaker detection (ASD). The ASD directly provides the
frame-level activity of the target speaker, while its intermediate feature
representation is trained to discriminate speech-lip synchronization that could
be used for speaker disentanglement. Experimental results show our model
outperforms baselines across various overlapping ratios, achieving an average
improvement of more than 4 dB in terms of SI-SNR.
