  Speaker-independent speech recognition systems trained with data from many
users are generally robust against speaker variability and work well for a
large population of speakers. However, these systems do not always generalize
well for users with very different speech characteristics. This issue can be
addressed by building personalized systems that are designed to work well for
each specific user. In this paper, we investigate the idea of securely training
personalized end-to-end speech recognition models on mobile devices so that
user data and models never leave the device and are never stored on a server.
We study how the mobile training environment impacts performance by simulating
on-device data consumption. We conduct experiments using data collected from
speech impaired users for personalization. Our results show that
personalization achieved 63.7\% relative word error rate reduction when trained
in a server environment and 58.1% in a mobile environment. Moving to on-device
personalization resulted in 18.7% performance degradation, in exchange for
improved scalability and data privacy. To train the model on device, we split
the gradient computation into two and achieved 45% memory reduction at the
expense of 42% increase in training time.
