# L-SpEx: Localized Target Speaker Extraction

arXiv: https://arxiv.org/abs/2202.09995 · HTML: https://mlsanity.com/paper/2202.09995/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2022.

## Built on by (most cited first, 9 of 9)

- [Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction](https://mlsanity.com/paper/2310.07284/) (2023)
- [MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources](https://mlsanity.com/paper/2207.07307/) (2022)
- [Steering Deep Non-Linear Spatially Selective Filters for Weakly Guided Extraction of Moving Speakers in Dynamic Scenarios](https://mlsanity.com/paper/2505.14517/) (2025)
- [Contextual Speech Extraction: Leveraging Textual History as an Implicit Cue for Target Speech Extraction](https://mlsanity.com/paper/2503.08798/) (2025)
- [Iterative Sound Source Localization for Unknown Number of Sources](https://mlsanity.com/paper/2206.12273/) (2022)
- [VCSE: Time-Domain Visual-Contextual Speaker Extraction Network](https://mlsanity.com/paper/2210.06177/) (2022)
- [MIMO-DBnet: Multi-channel Input and Multiple Outputs DOA-aware Beamforming Network for Speech Separation](https://mlsanity.com/paper/2212.03401/) (2022)
- [Mask-Weighted Spatial Likelihood Coding for Speaker-Independent Joint Localization and Mask Estimation](https://mlsanity.com/paper/2410.19595/) (2024)
- [End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer Functions](https://mlsanity.com/paper/2502.06285/) (2025)

## Abstract (arXiv)

Speaker extraction aims to extract the target speaker's voice from a multi-talker speech mixture given an auxiliary reference utterance. Recent studies show that speaker extraction benefits from the location or direction of the target speaker. However, these studies assume that the target speaker's location is known in advance or detected by an extra visual cue, e.g., face image or video. In this paper, we propose an end-to-end localized target speaker extraction on pure speech cues, that is called L-SpEx. Specifically, we design a speaker localizer driven by the target speaker's embedding to extract the spatial features, including direction-of-arrival (DOA) of the target speaker and beamforming output. Then, the spatial cues and target speaker's embedding are both used to form a top-down auditory attention to the target speaker. Experiments on the multi-channel reverberant dataset called MC-Libri2Mix show that our L-SpEx approach significantly outperforms the baseline system.