  Self- and semi-supervised learning methods have been actively investigated to
reduce labeled training data or enhance the model performance. However, the
approach mostly focus on in-domain performance for public datasets. In this
study, we utilize the combination of self- and semi-supervised learning methods
to solve unseen domain adaptation problem in a large-scale production setting
for online ASR model. This approach demonstrates that using the source domain
data with a small fraction of the target domain data (3%) can recover the
performance gap compared to a full data baseline: relative 13.5% WER
improvement for target domain data.
