VCSE: Time-Domain Visual-Contextual Speaker Extraction Network

• URL: http://arxiv.org/abs/2210.06177v1
• Date: Sun, 9 Oct 2022 12:29:38 GMT
• Title: VCSE: Time-Domain Visual-Contextual Speaker Extraction Network
• Authors: Junjie Li, Meng Ge, Zexu Pan, Longbiao Wang, Jianwu Dang
• Abstract summary: We propose a two-stage time-domain visual-contextual speaker extraction network named VCSE. 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.
• Score: 54.67547526785552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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