  Speech command recognition (SCR) has been commonly used on resource
constrained devices to achieve hands-free user experience. However, in real
applications, confusion among commands with similar pronunciations often
happens due to the limited capacity of small models deployed on edge devices,
which drastically affects the user experience. In this paper, inspired by the
advances of discriminative training in speech recognition, we propose a novel
minimize sequential confusion error (MSCE) training criterion particularly for
SCR, aiming to alleviate the command confusion problem. Specifically, we aim to
improve the ability of discriminating the target command from other commands on
the basis of MCE discriminative criteria. We define the likelihood of different
commands through connectionist temporal classification (CTC). During training,
we propose several strategies to use prior knowledge creating a confusing
sequence set for similar-sounding command instead of creating the whole
non-target command set, which can better save the training resources and
effectively reduce command confusion errors. Specifically, we design and
compare three different strategies for confusing set construction. By using our
proposed method, we can relatively reduce the False Reject Rate~(FRR) by 33.7%
at 0.01 False Alarm Rate~(FAR) and confusion errors by 18.28% on our collected
speech command set.
