  Voice assistants have become an essential tool for people with various
disabilities because they enable complex phone- or tablet-based interactions
without the need for fine-grained motor control, such as with touchscreens.
However, these systems are not tuned for the unique characteristics of
individuals with speech disorders, including many of those who have a
motor-speech disorder, are deaf or hard of hearing, have a severe stutter, or
are minimally verbal. We introduce an alternative voice-based input system
which relies on sound event detection using fifteen nonverbal mouth sounds like
"pop," "click," or "eh." This system was designed to work regardless of ones'
speech abilities and allows full access to existing technology. In this paper,
we describe the design of a dataset, model considerations for real-world
deployment, and efforts towards model personalization. Our fully-supervised
model achieves segment-level precision and recall of 88.6% and 88.4% on an
internal dataset of 710 adults, while achieving 0.31 false positives per hour
on aggressors such as speech. Five-shot personalization enables satisfactory
performance in 84.5% of cases where the generic model fails.
