  RemixIT and Remixed2Remixed are domain adaptation-based speech enhancement
(DASE) methods that use a teacher model trained in full supervision to generate
pseudo-paired data by remixing the outputs of the teacher model. The student
model for enhancing real-world recorded signals is trained using the
pseudo-paired data without ground truth. Since the noisy signals are recorded
in natural environments, the dataset inevitably suffers data imbalance in some
acoustic properties, leading to subpar performance for the underrepresented
data. The signal-to-noise ratio (SNR), inherently balanced in supervised
learning, is a prime example. In this paper, we provide empirical evidence that
the SNR of pseudo data has a significant impact on model performance using the
dataset of the CHiME-7 UDASE task, highlighting the importance of balanced SNR
in DASE. Furthermore, we propose adopting curriculum learning to encompass a
broad range of SNRs to boost performance for underrepresented data.
