  The Audio-Visual Speaker Extraction (AVSE) algorithm employs parallel video
recording to leverage two visual cues, namely speaker identity and
synchronization, to enhance performance compared to audio-only algorithms.
However, the visual front-end in AVSE is often derived from a pre-trained model
or end-to-end trained, making it unclear which visual cue contributes more to
the speaker extraction performance. This raises the question of how to better
utilize visual cues. To address this issue, we propose two training strategies
that decouple the learning of the two visual cues. Our experimental results
demonstrate that both visual cues are useful, with the synchronization cue
having a higher impact. We introduce a more explainable model, the Decoupled
Audio-Visual Speaker Extraction (DAVSE) model, which leverages both visual
cues.
