# MC-LExt: Multi-Channel Target Speaker Extraction with Onset-Prompted Speaker Conditioning Mechanism

arXiv: https://arxiv.org/abs/2510.15437 · HTML: https://mlsanity.com/paper/2510.15437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2025.

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

- [PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction](https://mlsanity.com/paper/2607.08111/) (2026)

## Abstract (arXiv)

Multi-channel target speaker extraction (MC-TSE) aims to extract a target speaker's voice from multi-speaker signals captured by multiple microphones. Existing methods often rely on auxiliary clues such as direction-of-arrival (DOA) or speaker embeddings. However, DOA-based approaches depend on explicit direction estimation and are sensitive to microphone array geometry, while methods based on speaker embeddings model speaker identity in an implicit manner and may degrade in noisy-reverberant conditions. To address these limitations, we propose multi-channel listen to extract (MC-LExt), a simple but highly-effective framework for MC-TSE. Our key idea is to prepend a short enrollment utterance of the target speaker to each channel of the multi-channel mixture, providing an onset-prompted conditioning signal that can guide TSE. This design allows the deep neural network (DNN) to learn spatial and speaker identity cues jointly in a fully end-to-end manner. Experiments on noisy-reverberant benchmarks, including WHAMR! and MC-Libri2Mix, demonstrate the effectiveness of MC-TSE.