  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.
