  Deaf or hard-of-hearing (DHH) speakers typically have atypical speech caused
by deafness. With the growing support of speech-based devices and software
applications, more work needs to be done to make these devices inclusive to
everyone. To do so, we analyze the use of openly-available automatic speech
recognition (ASR) tools with a DHH Japanese speaker dataset. As these
out-of-the-box ASR models typically do not perform well on DHH speech, we
provide a thorough analysis of creating personalized ASR systems. We collected
a large DHH speaker dataset of four speakers totaling around 28.05 hours and
thoroughly analyzed the performance of different training frameworks by varying
the training data sizes. Our findings show that 1000 utterances (or 1-2 hours)
from a target speaker can already significantly improve the model performance
with minimal amount of work needed, thus we recommend researchers to collect at
least 1000 utterances to make an efficient personalized ASR system. In cases
where 1000 utterances is difficult to collect, we also discover significant
improvements in using previously proposed data augmentation techniques such as
intermediate fine-tuning when only 200 utterances are available.
