  As speech-enabled devices such as smartphones and smart speakers become
increasingly ubiquitous, there is growing interest in building automatic speech
recognition (ASR) systems that can run directly on-device; end-to-end (E2E)
speech recognition models such as recurrent neural network transducers and
their variants have recently emerged as prime candidates for this task. Apart
from being accurate and compact, such systems need to decode speech with low
user-perceived latency (UPL), producing words as soon as they are spoken. This
work examines the impact of various techniques -- model architectures, training
criteria, decoding hyperparameters, and endpointer parameters -- on UPL. Our
analyses suggest that measures of model size (parameters, input chunk sizes),
or measures of computation (e.g., FLOPS, RTF) that reflect the model's ability
to process input frames are not always strongly correlated with observed UPL.
Thus, conventional algorithmic latency measurements might be inadequate in
accurately capturing latency observed when models are deployed on embedded
devices. Instead, we find that factors affecting token emission latency, and
endpointing behavior significantly impact on UPL. We achieve the best trade-off
between latency and word error rate when performing ASR jointly with
endpointing, and using the recently proposed alignment regularization.
