  Dysfluencies and variations in speech pronunciation can severely degrade
speech recognition performance, and for many individuals with
moderate-to-severe speech disorders, voice operated systems do not work.
Current speech recognition systems are trained primarily with data from fluent
speakers and as a consequence do not generalize well to speech with
dysfluencies such as sound or word repetitions, sound prolongations, or audible
blocks. The focus of this work is on quantitative analysis of a consumer speech
recognition system on individuals who stutter and production-oriented
approaches for improving performance for common voice assistant tasks (i.e.,
"what is the weather?"). At baseline, this system introduces a significant
number of insertion and substitution errors resulting in intended speech Word
Error Rates (isWER) that are 13.64\% worse (absolute) for individuals with
fluency disorders. We show that by simply tuning the decoding parameters in an
existing hybrid speech recognition system one can improve isWER by 24\%
(relative) for individuals with fluency disorders. Tuning these parameters
translates to 3.6\% better domain recognition and 1.7\% better intent
recognition relative to the default setup for the 18 study participants across
all stuttering severities.
