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The Audio-Visual Speaker Extraction (AVSE) algorithm employs parallel videorecording to leverage two visual cues, namely speaker identity andsynchronization, to enhance performance compared to audio-only algorithms.However, the visual front-end in AVSE is often derived from a pre-trained modelor end-to-end trained, making it unclear which visual cue contributes more tothe speaker extraction performance. This raises the question of how to betterutilize visual cues. To address this issue, we propose two training strategiesthat decouple the learning of the two visual cues. Our experimental resultsdemonstrate that both visual cues are useful, with the synchronization cuehaving a higher impact. We introduce a more explainable model, the DecoupledAudio-Visual Speaker Extraction (DAVSE) model, which leverages both visualcues.

Rethinking the visual cues in audio-visual speaker extraction