  In the speaker extraction problem, it is found that additional information
from the target speaker contributes to the tracking and extraction of the
target speaker, which includes voiceprint, lip movement, facial expression, and
spatial information. However, no one cares for the cue of sound onset, which
has been emphasized in the auditory scene analysis and psychology. Inspired by
it, we explicitly modeled the onset cue and verified the effectiveness in the
speaker extraction task. We further extended to the onset/offset cues and got
performance improvement. From the perspective of tasks, our onset/offset-based
model completes the composite task, a complementary combination of speaker
extraction and speaker-dependent voice activity detection. We also combined
voiceprint with onset/offset cues. Voiceprint models voice characteristics of
the target while onset/offset models the start/end information of the speech.
From the perspective of auditory scene analysis, the combination of two
perception cues can promote the integrity of the auditory object. The
experiment results are also close to state-of-the-art performance, using nearly
half of the parameters. We hope that this work will inspire communities of
speech processing and psychology, and contribute to communication between them.
Our code will be available in https://github.com/aispeech-lab/wase/.
