  This study investigates the performance of personalized automatic speech
recognition (ASR) for recognizing disordered speech using small amounts of
per-speaker adaptation data. We trained personalized models for 195 individuals
with different types and severities of speech impairment with training sets
ranging in size from <1 minute to 18-20 minutes of speech data. Word error rate
(WER) thresholds were selected to determine Success Percentage (the percentage
of personalized models reaching the target WER) in different application
scenarios. For the home automation scenario, 79% of speakers reached the target
WER with 18-20 minutes of speech; but even with only 3-4 minutes of speech, 63%
of speakers reached the target WER. Further evaluation found similar
improvement on test sets with conversational and out-of-domain, unprompted
phrases. Our results demonstrate that with only a few minutes of recordings,
individuals with disordered speech could benefit from personalized ASR.

Audio and Speech Processing;Computation and Language;Machine Learning;Sound