# Robust Speaker Extraction Network Based on Iterative Refined Adaptation

arXiv: https://arxiv.org/abs/2011.02102 · HTML: https://mlsanity.com/paper/2011.02102/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Self-Supervised Disentangled Representation Learning for Robust Target Speech Extraction](https://mlsanity.com/paper/2312.10305/) (2023)
- [Single microphone speaker extraction using unified time-frequency Siamese-Unet](https://mlsanity.com/paper/2203.02941/) (2022)
- [MoMuSE: Momentum Multi-modal Target Speaker Extraction for Real-time Scenarios with Impaired Visual Cues](https://mlsanity.com/paper/2412.08247/) (2024)
- [Contrastive Knowledge Distillation for Embedding Refinement in Personalized Speech Enhancement](https://mlsanity.com/paper/2601.16235/) (2026)

## Abstract (arXiv)

Speaker extraction aims to extract target speech signal from a multi-talker environment with interference speakers and surrounding noise, given the target speaker's reference information. Most speaker extraction systems achieve satisfactory performance on the premise that the test speakers have been encountered during training time. Such systems suffer from performance degradation given unseen target speakers and/or mismatched reference voiceprint information. In this paper we propose a novel strategy named Iterative Refined Adaptation (IRA) to improve the robustness and generalization capability of speaker extraction systems in the aforementioned scenarios. Given an initial speaker embedding encoded by an auxiliary network, the extraction network can obtain a latent representation of the target speaker, which is fed back to the auxiliary network to get a refined embedding to provide more accurate guidance for the extraction network. Experiments on WSJ0-2mix-extr and WHAM! dataset confirm the superior performance of the proposed method over the network without IRA in terms of SI-SDR and PESQ improvement.