# Plug-and-Steer: Decoupling Separation and Selection in Audio-Visual Target Speaker Extraction

arXiv: https://arxiv.org/abs/2603.19697 · HTML: https://mlsanity.com/paper/2603.19697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Adapting Personalized Speech Enhancement for Low-Latency Audio-Visual Target-Speaker Extraction](https://mlsanity.com/paper/2609.30631/) (2026)

## Abstract (arXiv)

The goal of this paper is to provide a new perspective on audio-visual target speaker extraction (AV-TSE) by decoupling separation and target selection. Conventional AV-TSE systems typically integrate audio and visual features deeply to re-learn the entire separation process, which can act as a fidelity ceiling due to the noisy nature of in-the-wild audio-visual datasets. To address this, we propose Plug-and-Steer, which assigns high-fidelity separation to a frozen audio-only backbone and limits the role of the visual modality strictly to target selection. We introduce the Latent Steering Matrix (LSM), a minimalist linear transformation that re-routes latent features within the backbone to anchor the target speaker to a designated channel. Experiments across four representative architectures show that our method effectively preserves the acoustic priors of diverse backbones, achieving perceptual quality comparable to that of the original backbones. Audio samples are available at: https://plugandsteer.github.io