# Probing Self-supervised Learning Models with Target Speech Extraction

arXiv: https://arxiv.org/abs/2402.13200 · HTML: https://mlsanity.com/paper/2402.13200/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2024.

## Built on by (most cited first, 5 of 5)

- [SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline](https://mlsanity.com/paper/2505.19314/) (2025)
- [TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models](https://mlsanity.com/paper/2505.06660/) (2025)
- [Iterative refinement, not training objective, makes HuBERT behave differently from wav2vec 2.0](https://mlsanity.com/paper/2508.08110/) (2025)
- [Investigation of Speaker Representation for Target-Speaker Speech Processing](https://mlsanity.com/paper/2410.11243/) (2024)
- [Incorporating Linguistic Constraints from External Knowledge Source for Audio-Visual Target Speech Extraction](https://mlsanity.com/paper/2506.09792/) (2025)

## Abstract (arXiv)

Large-scale pre-trained self-supervised learning (SSL) models have shown remarkable advancements in speech-related tasks. However, the utilization of these models in complex multi-talker scenarios, such as extracting a target speaker in a mixture, is yet to be fully evaluated. In this paper, we introduce target speech extraction (TSE) as a novel downstream task to evaluate the feature extraction capabilities of pre-trained SSL models. TSE uniquely requires both speaker identification and speech separation, distinguishing it from other tasks in the Speech processing Universal PERformance Benchmark (SUPERB) evaluation. Specifically, we propose a TSE downstream model composed of two lightweight task-oriented modules based on the same frozen SSL model. One module functions as a speaker encoder to obtain target speaker information from an enrollment speech, while the other estimates the target speaker's mask to extract its speech from the mixture. Experimental results on the Libri2mix datasets reveal the relevance of the TSE downstream task to probe SSL models, as its performance cannot be simply deduced from other related tasks such as speaker verification and separation.