  A speaker extraction algorithm seeks to extract the target speaker's speech
from a multi-talker speech mixture. The prior studies focus mostly on speaker
extraction from a highly overlapped multi-talker speech mixture. However, the
target-interference speaker overlapping ratios could vary over a wide range
from 0% to 100% in natural speech communication, furthermore, the target
speaker could be absent in the speech mixture, the speech mixtures in such
universal multi-talker scenarios are described as general speech mixtures. The
speaker extraction algorithm requires an auxiliary reference, such as a video
recording or a pre-recorded speech, to form top-down auditory attention on the
target speaker. We advocate that a visual cue, i.e., lip movement, is more
informative than an audio cue, i.e., pre-recorded speech, to serve as the
auxiliary reference for speaker extraction in disentangling the target speaker
from a general speech mixture. In this paper, we propose a universal speaker
extraction network with a visual cue, that works for all multi-talker
scenarios. In addition, we propose a scenario-aware differentiated loss
function for network training, to balance the network performance over
different target-interference speaker pairing scenarios. The experimental
results show that our proposed method outperforms various competitive baselines
for general speech mixtures in terms of signal fidelity.
