  Deep learning-based speech enhancement (SE) models have achieved impressive
performance in the past decade. Numerous advanced architectures have been
designed to deliver state-of-the-art performance; however, their scalability
potential remains unrevealed. Meanwhile, the majority of research focuses on
small-sized datasets with restricted diversity, leading to a plateau in
performance improvement. In this paper, we aim to provide new insights for
addressing the above issues by exploring the scalability of SE models in terms
of architectures, model sizes, compute budgets, and dataset sizes. Our
investigation involves several popular SE architectures and speech data from
different domains. Experiments reveal both similarities and distinctions
between the scaling effects in SE and other tasks such as speech recognition.
These findings further provide insights into the under-explored SE directions,
e.g., larger-scale multi-domain corpora and efficiently scalable architectures.
