  Supervised speech enhancement models are trained using artificially generated
mixtures of clean speech and noise signals, which may not match real-world
recording conditions at test time. This mismatch can lead to poor performance
if the test domain significantly differs from the synthetic training domain.
This paper introduces the unsupervised domain adaptation for conversational
speech enhancement (UDASE) task of the 7th CHiME challenge. This task aims to
leverage real-world noisy speech recordings from the target domain for
unsupervised domain adaptation of speech enhancement models. The target domain
corresponds to the multi-speaker reverberant conversational speech recordings
of the CHiME-5 dataset, for which the ground-truth clean speech reference is
unavailable. Given a CHiME-5 recording, the task is to estimate the clean,
potentially multi-speaker, reverberant speech, removing the additive background
noise. We discuss the motivation for the CHiME-7 UDASE task and describe the
data, the task, and the baseline system.
