  Self-supervised learning (SSL) has achieved great success in various areas
including speech processing. Recently, it is proven that speech based SSL
models are able to extract superior universal representations on a range of
downstream tasks compared to traditional hand-craft feature (e.g. FBank, MFCC)
in the SUPERB benchmark. However, different types of SSL models might exhibit
distinct strengths on different downstream tasks. In order to better utilize
the potential power of SSL models, in this work, we explore the effective
fusion on multiple SSL models. A series of model fusion algorithms are
investigated and compared by combining two types of SSL models, Hubert and
Data2vec, on two representative tasks from SUPERB benchmark, which are speaker
identification (SID) and automatic speech recognition (ASR) tasks. The
experimental results demonstrate that our proposed fusion algorithms can
further boost the individual model significantly.
