  The demand for fast and accurate incremental speech recognition increases as
the applications of automatic speech recognition (ASR) proliferate. Incremental
speech recognizers output chunks of partially recognized words while the user
is still talking. Partial results can be revised before the ASR finalizes its
hypothesis, causing instability issues. We analyze the quality and stability of
on-device streaming end-to-end (E2E) ASR models. We first introduce a novel set
of metrics that quantify the instability at word and segment levels. We study
the impact of several model training techniques that improve E2E model
qualities but degrade model stability. We categorize the causes of instability
and explore various solutions to mitigate them in a streaming E2E ASR system.
Index Terms: ASR, stability, end-to-end, text normalization,on-device, RNN-T
