  Practitioners often need to build ASR systems for new use cases in a short
amount of time, given limited in-domain data. While recently developed
end-to-end methods largely simplify the modeling pipelines, they still suffer
from the data sparsity issue. In this work, we explore a few
simple-to-implement techniques for building online ASR systems in an end-to-end
fashion, with a small amount of transcribed data in the target domain. These
techniques include data augmentation in the target domain, domain adaptation
using models previously trained on a large source domain, and knowledge
distillation on non-transcribed target domain data, using an adapted
bi-directional model as the teacher; they are applicable in real scenarios with
different types of resources. Our experiments demonstrate that each technique
is independently useful in the improvement of the online ASR performance in the
target domain.
