  Nowadays, speech is becoming a more common, if not standard, interface to
technology. This can be seen in the trend of technology changes over the years.
Increasingly, voice is used to control programs, appliances and personal
devices within homes, cars, workplaces, and public spaces through smartphones
and home assistant devices using Amazon's Alexa, Google's Assistant and Apple's
Siri, and other proliferating technologies. However, most speech interfaces are
not accessible for Deaf and Hard-of-Hearing (DHH) people. In this paper,
performances of current Automatic Speech Recognition (ASR) with voices of DHH
speakers are evaluated. ASR has improved over the years, and is able to reach
Word Error Rates (WER) as low as 5-6% [1][2][3], with the help of
cloud-computing and machine learning algorithms that take in custom vocabulary
models. In this paper, a custom vocabulary model is used, and the significance
of the improvement is evaluated when using DHH speech.
