  While current state-of-the-art Automatic Speech Recognition (ASR) systems
achieve high accuracy on typical speech, they suffer from significant
performance degradation on disordered speech and other atypical speech
patterns. Personalization of ASR models, a commonly applied solution to this
problem, is usually performed in a server-based training environment posing
problems around data privacy, delayed model-update times, and communication
cost for copying data and models between mobile device and server
infrastructure. In this paper, we present an approach to on-device based ASR
personalization with very small amounts of speaker-specific data. We test our
approach on a diverse set of 100 speakers with disordered speech and find
median relative word error rate improvement of 71% with only 50 short
utterances required per speaker. When tested on a voice-controlled home
automation platform, on-device personalized models show a median task success
rate of 81%, compared to only 40% of the unadapted models.
