VCSE: Time-Domain Visual-Contextual Speaker Extraction Network
Abstract
Speaker extraction seeks to extract the target speech in a multi-talker scenario given an auxiliary reference. Such reference can be auditory, i.e., a pre-recorded speech, visual, i.e., lip movements, or contextual, i.e., phonetic sequence. References in different modalities provide distinct and complementary information that could be fused to form top-down attention on the target speaker. Previous studies have introduced visual and contextual modalities in a single model. In this paper, we propose a two-stage time-domain visual-contextual speaker extraction network named VCSE, which incorporates visual and self-enrolled contextual cues stage by stage to take full advantage of every modality. In the first stage, we pre-extract a target speech with visual cues and estimate the underlying phonetic sequence. In the second stage, we refine the pre-extracted target speech with the self-enrolled contextual cues. Experimental results on the real-world Lip Reading Sentences 3 (LRS3) database demonstrate that our proposed VCSE network consistently outperforms other state-of-the-art baselines.
Cite
@article{arxiv.2210.06177,
title = {VCSE: Time-Domain Visual-Contextual Speaker Extraction Network},
author = {Junjie Li and Meng Ge and Zexu Pan and Longbiao Wang and Jianwu Dang},
journal= {arXiv preprint arXiv:2210.06177},
year = {2022}
}